Friend account recommendation method and device, equipment and storage medium

Abstract

The invention discloses a friend account recommendation method and device, equipment and a storage medium, and belongs to the field of internet social contact. The method comprises the following steps: acquiring a current account; calculating ranking list participation proportions of the current account for the at least two ranking lists, wherein the ranking list participation proportion of each ranking list is used for indicating the proportion of the participation degree of the current account for the game playing method of the ranking list to the total participation degree; obtaining potential friend accounts respectively corresponding to the current account in the at least two ranking lists; according to the ranking list participation proportion of the current account for each ranking list, determining the sub recommendation quantity of each ranking list; and based on the sub recommendation number of each ranking list, extracting a target friend account from the potential friend accounts corresponding to the at least two ranking lists, and generating a friend recommendation list. In the application, the potential friends are obtained according to the game data of the current account, so that the quality of the potential friends can be improved.

Description

Technical Field

[0001] The present application relates to the field of Internet social networking, and in particular to a friend recommendation method, device, equipment and storage medium. Background Art

[0002] With the rapid popularization of smart devices, the gaming industry is booming. In order to encourage players to actively participate in gaming activities and increase their desire to compete, different rankings are often set for different game modes. And as the player group becomes larger and larger, the amount of data for each ranking is often in the millions or even tens of millions. Therefore, in order to improve the quality of service (QoS) of the game, multiple processes are often allocated to the rankings, with each ranking occupying a process or several rankings sharing a process. In addition to the rankings, the friend system is also an important part of the game. In order to promote players to socialize in the game and improve player retention, it is necessary to recommend like-minded friends to players, and like-minded friends that players have not yet discovered can be called potential friends. Since the game rankings can largely reflect the player's inclination towards different gameplays of the game, the rankings can be combined to recommend potential friends to players.

[0003] In the related art, since there are multiple ranking processes, potential friends of the player who are ranked similar to the player in multiple rankings are obtained respectively. Since the deployment method and network conditions of each ranking process are different, when requesting potential friends through the ranking process, each ranking process returns the potential friends in a different order, and then the potential friends returned first are put into the friend recommendation list according to the order in which each ranking process returns. Since the order of potential friends in the friend recommendation list only depends on the return order of the corresponding ranking process, even if the player ranks very low in a certain ranking, the potential friends in the ranking may be displayed in front of the friend recommendation list. In this way, the quality of the potential friends obtained is uneven, that is, the potential friends actually obtained may not be friends who truly share the same interests as the player.

[0004] Therefore, how to improve the friend recommendation method based on the ranking list and improve the quality of potential friends is an urgent problem that needs to be solved. Summary of the invention

[0005] The embodiment of the present application provides a friend account recommendation method, device, equipment and storage medium. The technical solution is as follows:

[0006] On the one hand, an embodiment of the present application provides a friend account recommendation method, the method is executed by a server, and the method includes:

[0007] Obtaining a current account, wherein the current account participates in a game having at least two leaderboards on a game platform;

[0008] Calculating the ranking participation ratio of the current account for the at least two rankings, the ranking participation ratio of each ranking is used to indicate the ratio of the current account's participation level in the gameplay of the ranking to the total participation level, the total participation level is used to indicate the participation level of the current account in the gameplay of the at least two rankings;

[0009] Obtaining potential friend accounts corresponding to the current account in the at least two ranking lists respectively;

[0010] Determine the number of sub-recommendations for each ranking list according to the ranking list participation ratio of the current account for each ranking list, wherein the number of sub-recommendations is used to indicate the number of potential friend accounts that need to be extracted from the ranking list;

[0011] Based on the number of sub-recommendations of each of the ranking lists, target friend accounts are extracted from the potential friend accounts respectively corresponding to the at least two ranking lists to generate a friend recommendation list.

[0012] On the other hand, an embodiment of the present application provides a device for recommending friend accounts, the device comprising:

[0013] A first acquisition module is used to acquire a current account, wherein the current account participates in a game having at least two rankings in the game platform;

[0014] A calculation module, used to calculate the ranking participation percentage of the current account for the at least two rankings, wherein the ranking participation percentage of each ranking is used to indicate the participation level of the current account in the game play of the ranking;

[0015] A second acquisition module is used to acquire potential friend accounts corresponding to the current account in the at least two rankings respectively;

[0016] A determination module, configured to determine the number of sub-recommendations for each of the rankings according to the current account's ranking participation ratio for each of the rankings, wherein the number of sub-recommendations is used to indicate the number of potential friend accounts that need to be extracted from the rankings;

[0017] The generating module is used to extract target friend accounts from the potential friend accounts respectively corresponding to the at least two ranking lists based on the number of sub-recommendations of each ranking list, and generate a friend recommendation list.

[0018] On the other hand, an embodiment of the present application provides a computer device, which includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement a friend account recommendation method.

[0019] On the other hand, an embodiment of the present application provides a non-temporary computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, a method for recommending friend accounts is implemented.

[0020] On the other hand, an embodiment of the present application provides a computer program product, which includes a computer program, and the computer program is stored in a computer-readable storage medium; the computer program is read and executed from the computer-readable storage medium by a processor of a computer device, so that the computer device executes a method for recommending friend accounts.

[0021] The technical solution provided in the embodiments of the present application can bring the following beneficial effects:

[0022] The above technical solution generates a friend recommendation list by extracting the target friend account from the potential friend accounts obtained from each ranking list according to the participation ratio of the ranking list corresponding to the current account. Among them, the participation ratio of the ranking list is used to indicate the degree of participation of the current account in the gameplay of the ranking list. The user's interest in at least two ranking lists is judged according to the degree of participation of the user in the gameplay of at least two ranking lists. The higher the degree of participation in the gameplay, the higher the user's interest in the gameplay. Therefore, when choosing to recommend a friend account, more potential friend accounts on the ranking list corresponding to the gameplay that the user is interested in will be recommended, that is, the larger the number of sub-recommendations corresponding to the ranking list. In the friend recommendation list obtained in this way, the overall quality of the potential friend accounts is higher because the proportion of potential friend accounts on the ranking list corresponding to the gameplay of interest is larger. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A schematic diagram showing a friend account recommendation method provided by an exemplary embodiment of the present application;

[0025] Figure 2 A schematic diagram of a computer system provided by an exemplary embodiment of the present application is shown;

[0026] Figure 3 A flowchart of a friend account recommendation method provided by an exemplary embodiment of the present application is shown;

[0027] Figure 4 A flowchart of a friend account recommendation method provided by an exemplary embodiment of the present application is shown;

[0028] Figure 5 A flowchart of a friend account recommendation method provided by an exemplary embodiment of the present application is shown;

[0029] Figure 6 A flowchart of a friend account recommendation method provided by an exemplary embodiment of the present application is shown;

[0030] Figure 7 A flowchart of a friend account recommendation method provided by an exemplary embodiment of the present application is shown;

[0031] Figure 8 A schematic diagram showing a friend account recommendation method provided by an exemplary embodiment of the present application;

[0032] Fig. 9 A flowchart of a friend account recommendation method provided by an exemplary embodiment of the present application is shown;

[0033] Fig.10 A flowchart of a friend account recommendation method provided by an exemplary embodiment of the present application is shown;

[0034] Fig.11 A flowchart of a friend account recommendation method provided by an exemplary embodiment of the present application is shown;

[0035] Fig.12 A flowchart of a friend account recommendation method provided by an exemplary embodiment of the present application is shown;

[0036] Fig.13 A flowchart of a friend account recommendation method provided by an exemplary embodiment of the present application is shown;

[0037] Fig.14 A structural block diagram of a friend account recommendation device provided by an exemplary embodiment of the present application is shown;

[0038] Fig.15 A structural block diagram of a friend account recommendation device provided by an exemplary embodiment of the present application is shown;

[0039] Fig.16 A structural block diagram of a friend account recommendation device provided by an exemplary embodiment of the present application is shown;

[0040] Fig.17 A structural block diagram of a friend account recommendation device provided by an exemplary embodiment of the present application is shown;

[0041] Fig.18 A structural block diagram of a server provided by an exemplary embodiment of the present application is shown. DETAILED DESCRIPTION

[0042] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0043] First, the relevant terms involved in this application are introduced:

[0044] Engagement: refers to the time and / or number of times a user spends playing a game. The user's engagement with different game modes often reflects the user's preference for the game. You can customize personalized user plans based on the user's engagement with different game modes to improve the user experience.

[0045] Ranking list: quantitative ranking information of the strength of similar things. Game developers introduce ranking lists into games to increase players' enthusiasm for participating in game activities. When a player's account is at the top of the ranking list, it can satisfy the player's vanity to a certain extent, and at the same time stimulate the desire to win of other players who are ranked lower, so that they actively participate in game activities to improve the ranking of the ranking list, thereby increasing the online rate and popularity of the game.

[0046] Account: User identity authentication on the network platform. With the popularization of the Internet, people's social activities have gradually shifted to the Internet. When socializing, it is often necessary to use a unified identifier to distinguish the information of different users. The network platform often calls this unified identifier an account.

[0047] Potential friend account: A player account whose possibility of establishing a friendship with the current account reaches a certain threshold. Potential friends are people who may become friends, and the quantified representation is people whose possibility of establishing a friendship reaches a certain threshold. Generally speaking, the nature of potential friend accounts varies depending on the nature of the social platform. For example, social tool platforms are more suitable for socializing with acquaintances, so potential friend accounts are often people you may know; social game platforms are more suitable for socializing with game enthusiasts, so potential friend accounts tend to be people with the same interests.

[0048] Friend recommendation: A method of recommending potential friend accounts to a certain account. Friend recommendation was initially a user recommending his or her friends to another user, but with the development of computer technology, computer systems can also recommend friends to a certain account according to designed algorithms. Depending on the type of recommended friends, different algorithms can be designed for more accurate recommendations. For example, when recommending potential friends that you may know, you can recommend by building a friend relationship graph; when recommending potential friends with the same interests, you can recommend by using friend tags or recording account activity data.

[0049] Figure 1 A schematic diagram of a friend account recommendation method provided by an exemplary embodiment of the present application is shown.

[0050] like Figure 1 As shown, the current account is ranked in 3 rankings, namely Ranking A, Ranking B and Ranking C. The current account ranks 45th in Ranking A, 4th in Ranking B, and 85th in Ranking C. Figure 1 Schematic diagram (1) shows the partial ranking of accounts in ranking list A, schematic diagram (2) shows the partial ranking of accounts in ranking list B, and schematic diagram (2) shows the partial ranking of accounts in ranking list C. When the current account recommends a friend account, the total number of recommendations in the friend recommendation list 20 is 6, that is, the friend recommendation list has 5 friend positions 21 to 26 respectively.

[0051] Step 1, according to the ranking list of the current account, obtain the potential friend accounts 27 that meet the first quantity in each ranking list.

[0052] The first number is equal to the total number of recommendations. Since the current account ranks 45th in ranking list A, 4th in ranking list B, and 85th in ranking list C, 6 potential friend accounts are obtained from ranking list A, ranking list B, and ranking list C respectively. The rankings of these 6 potential friend accounts in the corresponding ranking lists are close to that of the current account.

[0053] Step 2, calculate the ranking participation ratio of each ranking, where the ranking participation ratio is used to indicate the ratio of the current account's participation level in the gameplay of the ranking to the participation level in the gameplay of all rankings in which the current account participates.

[0054] The ranking participation percentage is calculated based on the sum of the number of ranking refreshes and gameplay participation for each ranking for the current account.

[0055] The calculated ranking participation ratio of ranking A is 1 / 3, the calculated ranking participation ratio of ranking B is 1 / 2, and the calculated ranking participation ratio of ranking C is 1 / 6.

[0056] Step 3, determine the number of sub-recommendations 27 for each ranking list based on the ranking participation ratio and the total number of recommendations for each ranking list.

[0057] Calculate the product of the ranking participation percentage and the total number of recommendations for each ranking to determine the number of sub-recommendations for each ranking27.

[0058] Among them, the number of sub-recommendations in ranking list A is 6*1 / 3, and the result is 2; the number of sub-recommendations in ranking list B is 6*1 / 2, and the result is 3; the number of sub-recommendations in ranking list C is 6*1 / 6, and the result is 1.

[0059] Step 4: Obtain potential friend accounts from each ranking list in order of ranking and generate a friend recommendation list.

[0060] Prioritize obtaining potential friend accounts from rankings with a high participation rate, that is, first obtain potential friend accounts from ranking B. Among them, the order of extracting potential friend accounts is to prioritize extracting potential friend accounts with a ranking close to the current account, that is, prioritize extracting accounts with a smaller absolute value of the difference between the ranking of the ranking and the ranking of the potential friend account; when the absolute values ​​of the differences are the same, prioritize extracting accounts with a higher ranking.

[0061] First, 29 takes out the potential friend account a3 ranked 3, and compares it with the potential friend account [] saved in the friend recommendation list 20. If no potential friend account is found to be repeated with the potential friend account a3, the potential friend account a3 is added to the friend position 21 of the friend recommendation list 20. At this time, the number of potential friend accounts from the ranking list B in the friend recommendation list is 1, which is not greater than the number of sub-recommendations of the ranking list B. 30 continues to extract the next potential friend account a7 from the ranking list B to determine whether it is repeated with the potential friend account [a3] in the friend recommendation list 20. If no duplication is found, the potential friend account a7 is added to the friend position 21 of the friend recommendation list 20. Potential friend account a7 is added to friend position 22 of friend recommendation list 20; at this time, the number of potential friend accounts from ranking list B in the friend recommendation list is 2, which is not greater than the number of sub-recommendations of ranking list B. 31 continues to extract the next potential friend account a2 from ranking list B, and determines whether it is repeated with potential friend accounts [a3, a7] in friend recommendation list 20. If no duplication is found, potential friend account a2 is added to friend position 23 of friend recommendation list 20; at this time, the number of potential friend accounts from ranking list B in the friend recommendation list is 3, which is equal to the number of sub-recommendations of ranking list B.

[0062] The potential friend account is obtained from the ranking list with the second highest participation rate, that is, the potential friend account is obtained from ranking list A. First, 32 takes out the potential friend account a5 ranked 44, and determines whether it is repeated with the potential friend account [a3, a7, a2] in the friend recommendation list 20. If no duplication is found, the potential friend account a5 is added to the friend position 24 of the friend recommendation list 20; at this time, the number of potential friend accounts from ranking list A in the friend recommendation list is 1, which is not greater than the number of sub-recommendations of ranking list A. 33 continues to extract the next potential friend account a6 from ranking list A, and determines whether it is repeated with the potential friend account [a3, a7, a2, a5] in the friend recommendation list 20. If no duplication is found, the potential friend account a6 is added to the friend position 25 of the friend recommendation list 20; at this time, the number of potential friend accounts from ranking list A in the friend recommendation list is 2, which is equal to the number of sub-recommendations of ranking list A.

[0063] The potential friend account is obtained from the ranking list with the third highest participation rate, that is, the potential friend account is obtained from ranking list C. First, 34 takes out the potential friend account a3 ranked 84, and determines whether it is repeated with the potential friend accounts [a3, a7, a2, a5, a6] in the friend recommendation list 20. If it is repeated, 35 re-acquires the next potential friend account a6, and determines whether it is repeated with the potential friend accounts [a3, a7, a2, a5, a6] in the friend recommendation list 20. If it is repeated, 36 re-acquires the next potential friend account a13, and determines whether it is repeated with the potential friend accounts [a3, a7, a2, a5, a6] in the friend recommendation list 20. If no duplication is found, the potential friend account a13 is added to the friend position 26 of the friend recommendation list 20; at this time, the potential friend account from the ranking list C in the friend recommendation list is 1, which is equal to the number of sub-recommendations of the ranking list C, and at this time the number of potential friend accounts in the friend recommendation list has reached the total number of recommendations, and a final friend recommendation list 20 is generated, and the friend accounts in the final friend recommendation list are [a3, a7, a2, a5, a6, a13].

[0064] Figure 2 A schematic diagram of a computer system provided by an exemplary embodiment of the present application is shown. The computer system may include: a server 110 and a terminal device 120.

[0065] Server 110 can be any one of a plurality of servers, virtual cloud storage or cloud computing center. Server 110 is used to obtain game data of an account, and the game data of an account includes at least one of: account, ranking of a ranking list for each ranking list, participation in gameplay for each ranking list, and number of ranking list refreshes for each ranking list; Server 110 is also used to obtain potential friend accounts, which are potential friend accounts corresponding to each ranking list obtained based on game data; Server 110 is also used to select a target friend account from the obtained potential friend accounts based on game data, and generate a friend recommendation list. Optionally, server 110 includes a lobby process and a ranking process, and the lobby process is used to generate a friend recommendation list based on game data; the lobby process is also used to communicate with terminal device 120. The ranking process is used to obtain potential friend accounts in each ranking list.

[0066] The terminal device 120 may be a laptop computer, a desktop computer, a mobile phone, a tablet computer, an e-book reader, an electronic game console, etc.

[0067] The terminal device 120 includes a memory and a processor; the memory may include one or more computer-readable storage media. The computer-readable storage media include at least one of a random access memory (RAM), a read-only memory (ROM), and a flash memory. An operating system and a game platform are installed in the memory.

[0068] The game platform can be any one of level-breaking games, casual competitive games, MMOG (Massive Multiplayer Online Game) games, board games, MOBA (Multiplayer Online Battle Arena) games, SLG (Simulation Game) games, virtual reality applications, three-dimensional map programs, FPS (First-Person Shooting Game) games, multiplayer gunfight survival games, casual games, party games, and sandbox games.

[0069] The operating system is the basic software that provides the gaming platform with secure access to computer hardware. The operating system can be Android or iOS. The operating system supports the download, installation and operation of the gaming platform.

[0070] The terminal device 120 is installed and running a game platform, which includes at least two leaderboards.

[0071] The terminal device 120 is a terminal device used by the user. The terminal device 120 is used to display the friend recommendation list from the server 110 to the user. Optionally, the user requests the server 110 to recommend a friend account through the terminal device 120.

[0072] The server 110 and the terminal device 120 are connected via a wired or wireless network.

[0073] It should be noted that, in some embodiments, the server 110 and the terminal device 120 are the same device.

[0074] Figure 3 A flowchart of a friend account recommendation method provided by an exemplary embodiment of the present application is shown. The method is executed by a server, which may be Figure 2 The method comprises:

[0075] Step 210: Obtain the current account, where the current account participates in a game with at least two leaderboards on the game platform;

[0076] In some embodiments, a current account is obtained, where the current account is an account that needs to be recommended as a friend account.

[0077] In some embodiments, in response to a trigger operation on a friend recommendation control, the terminal device sends a request for acquisition to the server, where the request for acquisition is used to request the server for a friend recommendation list, and the request for acquisition carries the current account; or, in response to a friend recommendation operation triggered by the terminal device, the terminal device sends a request for acquisition to the server, where the request for acquisition is used to request the server for a friend recommendation list, and the request for acquisition carries the current account; or, in response to a friend recommendation operation of the server, the server acquires an account that needs to be recommended as a friend account from a database, and the account is the current account.

[0078] In some embodiments, the current account is pre-stored by the terminal device; or, the current account is obtained by the terminal device from the game platform; or, the current account is obtained by the server from a database.

[0079] Exemplarily, when a user logs in to a game platform via a terminal device, the terminal device obtains the account used by the user to log in to the game platform and saves it as the current account; or, when a friend account needs to be recommended, the terminal device requests the game platform to obtain the current account; or, after the terminal device obtains the current account from the game platform, the current account is saved to the terminal device for use when recommending a friend account again later.

[0080] Step 220: Calculate the ranking participation percentage of the current account for at least two rankings, where the ranking participation percentage of each ranking is used to indicate the percentage of the current account's participation in the gameplay of the ranking to the total participation percentage, and the total participation percentage is used to indicate the participation percentage of the current account in the gameplay of at least two rankings in which the current account participates;

[0081] In some embodiments, a leaderboard corresponds to at least one gameplay.

[0082] Exemplarily, the game platform has three rankings, namely the popularity ranking, the rank ranking and the combat power ranking. The gameplay corresponding to the popularity ranking is to change the costumes. The gameplay corresponding to the rank ranking is to participate in the ranking competition. The gameplay corresponding to the combat power ranking is to change equipment and upgrade equipment. The game platform supports controlling virtual characters to play in the virtual world. In order to meet the needs of different types of players, multi-dimensional gameplay is often designed. For example, the fashion gameplay is designed to meet the needs of players to dress up the virtual characters they control. The fashion gameplay may include changing fashion and designing fashion. The fashion may be a fashion that simply splits the dressing area, such as splitting it into hair and clothes; it may also be a detailed fashion, such as splitting it into hair, makeup, tops, bottoms, shoes, etc. Players can freely match the virtual characters they control to meet the aesthetic needs of players. The rank generally adopts an intuitive points mechanism, which is a form of expression of level; when a player participates in a certain game play, when he wins the game, he will get a certain amount of points, and when he loses the game, he will lose a certain amount of points. When the game play is ranked according to the amount of points, in order to obtain a higher ranking, it is necessary to participate in the game play. This game play can be called a ranking match, that is, a match for a better position. In order to stimulate players to actively participate in various game activities, the combat power mechanism will be involved. Combat power is combat power. The virtual character controlled by the player is numerically converted into combat power to show the strength of the virtual character controlled by the player. In addition to the numerical value of the virtual character itself, an equipment system will also be designed to enhance the combat power of the virtual character by wearing equipment with different combat power values ​​for the virtual character. At the same time, equipment upgrade gameplay will also be designed to enhance the combat power of the equipment by adding levels to the equipment, inlaying gems and inscriptions of different combat powers, etc., so that players can further gain the sense of accomplishment obtained in the game.

[0083] In some embodiments, when the current account participates in the game play, game data will be generated. If the game platform has established a ranking list for the game play, corresponding ranking data will be generated on the ranking list.

[0084] In some embodiments, the game platform will set up a ranking list, and the ranking list types corresponding to the ranking list include: at least one of a social ranking list, an entertainment ranking list, a combat ranking list, a leisure ranking list, an activity ranking list, and a points ranking list.

[0085] For example, the popularity ranking belongs to the social ranking; the ranking belongs to the points ranking; and the combat power ranking belongs to the combat ranking.

[0086] In some embodiments, the ranking participation percentages of the current account for at least two rankings are calculated respectively.

[0087] Step 230: Obtain potential friend accounts corresponding to the current account in at least two rankings;

[0088] Exemplarily, there are three rankings, namely Ranking A, Ranking B and Ranking C. Get the potential friend accounts corresponding to the current account in Ranking A, get the potential friend accounts corresponding to the current account in Ranking B, and get the potential friend accounts corresponding to the current account in Ranking C.

[0089] In some embodiments, the potential friend account corresponding to each acquired ranking list is at least one potential friend account.

[0090] Exemplarily, there are three rankings, namely Ranking A, Ranking B and Ranking C. Get the 10 potential friend accounts corresponding to the current account in Ranking A, get the 10 potential friend accounts corresponding to the current account in Ranking B, and get the 8 potential friend accounts corresponding to the current account in Ranking C; or, get the 2 potential friend accounts corresponding to the current account in Ranking A, get the 3 potential friend accounts corresponding to the current account in Ranking B, and get the 20 potential friend accounts corresponding to the current account in Ranking C; or, get the 10 potential friend accounts corresponding to the current account in Ranking A, get the 10 potential friend accounts corresponding to the current account in Ranking B, and get the 10 potential friend accounts corresponding to the current account in Ranking C.

[0091] In some embodiments, the potential friend account includes at least one of: a potential friend account number, a potential friend account name, and an online status of the potential friend account.

[0092] Step 240: Determine the number of sub-recommendations for each ranking list based on the current account's ranking list participation ratio for each ranking list, where the number of sub-recommendations is used to indicate the number of potential friend accounts that need to be extracted from the ranking list;

[0093] For example, there are three rankings, and the current account's ranking participation ratio for each ranking is 0.2, 0.3, and 0.5 respectively. According to the current account's ranking participation ratio for each ranking, the number of sub-recommendations for each ranking is determined to be 2, 3, and 5 respectively; or, according to the current account's ranking participation ratio for each ranking, the number of sub-recommendations for each ranking is determined to be 4, 6, and 10 respectively; or, according to the current account's ranking participation ratio for each ranking, the number of sub-recommendations for each ranking is determined to be 6, 9, and 15 respectively.

[0094] Step 250: based on the number of sub-recommendations of each ranking list, extract target friend accounts from the potential friend accounts corresponding to at least two ranking lists respectively, and generate a friend recommendation list.

[0095] In some embodiments, target friend accounts are extracted from potential friend accounts corresponding to at least two rankings to generate a friend recommendation list, and the number of target friend accounts extracted from each ranking meets the number of sub-recommendations of each ranking.

[0096] For example, there are two rankings, ranking A and ranking B, wherein the number of sub-recommendations of ranking A is 3, and the number of sub-recommendations of ranking B is 2. Three first target friend accounts a1 to a3 are extracted from the potential friend accounts corresponding to ranking A, and two second target friend accounts a4 and a5 are extracted from the potential friend accounts corresponding to ranking B, to generate a friend recommendation list, which has a total of 5 target friend accounts, namely a1 to a5.

[0097] In some embodiments, step 220 and step 230 may be performed in an interchangeable order or simultaneously.

[0098] In summary, the method provided in this embodiment generates a friend recommendation list by extracting a target friend account from the potential friend accounts obtained from each ranking list according to the participation ratio of the ranking list corresponding to the current account. Among them, the participation ratio of the ranking list is used to indicate the degree of participation of the current account in the gameplay of the ranking list. The user's interest in at least two ranking lists is confirmed based on the different degrees of participation of the user in the gameplay of at least two ranking lists. The higher the degree of participation in the gameplay, the higher the user's interest in the gameplay. Therefore, when choosing to recommend a friend account, more potential friend accounts on the ranking list corresponding to the gameplay that the user is interested in will be recommended, that is, the larger the number of sub-recommendations corresponding to the ranking list. In the friend recommendation list obtained in this way, since the potential friend accounts on the ranking list corresponding to the gameplay of interest account for a larger proportion, the overall quality of the potential friend accounts is higher.

[0099] Based on Figure 3 Based on the optional embodiment, Figure 4As shown, step 240 can be implemented alternatively as step 241 and step 242 .

[0100] Step 241: Obtain the total number of recommendations in the friend recommendation list;

[0101] In some embodiments, the total number of recommendations in the friend recommendation list is the total capacity of the friend recommendation list.

[0102] In some embodiments, the total number of recommendations in the friend recommendation list is a preset value; or, the total number of recommendations in the friend recommendation list is set by the user.

[0103] Exemplarily, the total number of recommendations in the friend recommendation list is a preset value of 20 set by a game platform designer; or, the total number of recommendations in the friend recommendation list is a value of 25 set by the user.

[0104] Step 242: Determine the number of sub-recommendations for each ranking list based on the current account's ranking list participation percentage and total number of recommendations for each ranking list.

[0105] For example, there are three rankings, namely, ranking A, ranking B and ranking C, and their corresponding ranking participation ratios are 0.2, 0.3, and 0.5, respectively. The total number of recommendations is 30. The number of sub-recommendations of ranking A is 6, the number of sub-recommendations of ranking B is 9, and the number of sub-recommendations of ranking C is 15; or, the number of sub-recommendations of ranking A is 12, the number of sub-recommendations of ranking B is 18, and the number of sub-recommendations of ranking C is 30; or, the number of sub-recommendations of ranking A is 2, the number of sub-recommendations of ranking B is 3, and the number of sub-recommendations of ranking C is 5.

[0106] In summary, the method provided in this embodiment determines the number of sub-recommendations for each ranking list based on the total number of recommendations in the friend recommendation list and the current account's ranking participation ratio for each ranking list, so that the number of friend accounts from each ranking list in the friend recommendation list is more reasonable.

[0107] Based on Figure 4 In an optional embodiment, if Figure 5 As shown, step 242 can be implemented alternatively as step 2421.

[0108] Step 2421: Determine the number of sub-recommendations for each ranking list based on the product of the current account's ranking list participation percentage and the total number of recommendations for each ranking list.

[0109] Exemplarily, there are three rankings, namely, ranking A, ranking B, and ranking C. The ranking participation ratios corresponding to the three rankings are 0.2, 0.3, and 0.5, respectively, and the total number of recommendations is 30; the number of sub-recommendations of ranking A is 6, the number of sub-recommendations of ranking B is 9, and the number of sub-recommendations of ranking C is 15; or, the ranking participation ratios corresponding to the three rankings are 0.2, 0.3, and 0.5, respectively, and the total number of recommendations is 20; the number of sub-recommendations of ranking A is 4, the number of sub-recommendations of ranking B is 6, and the number of sub-recommendations of ranking C is 10; or, the ranking participation ratios corresponding to the three rankings are 1 / 3, 1 / 2, and 1 / 6, respectively, and the total number of recommendations is 20; the number of sub-recommendations of ranking A is 7, the number of sub-recommendations of ranking B is 10, and the number of sub-recommendations of ranking C is 3.

[0110] In some embodiments, if the product of the current account's ranking participation percentage and the total number of recommendations for each ranking list is not an integer, it is rounded up.

[0111] For example, there are 3 rankings, and the corresponding ranking participation ratios of the 3 rankings are 1 / 3, 1 / 2, and 1 / 6 respectively, and the total number of recommendations is 20; the number of sub-recommendations of ranking A is 7, the number of sub-recommendations of ranking B is 10, and the number of sub-recommendations of ranking C is 3.

[0112] To sum up, the method provided in this embodiment determines the number of sub-recommendations for each ranking list based on the product of the total number of recommendations in the friend recommendation list and the current account's ranking participation ratio for each ranking list, so that the number of friend accounts from each ranking list in the friend recommendation list is more reasonable.

[0113] In some embodiments, the at least two leaderboards include an i-th leaderboard and an (i+1)-th leaderboard.

[0114] In some embodiments, the number of sub-recommendations of the i-th ranking list is greater than the number of sub-recommendations of the i+1-th ranking list.

[0115] In some embodiments, at least two ranking lists are sorted according to the number of sub-recommendations.

[0116] Based on Figure 3 In an optional embodiment, if Figure 6 As shown, step 250 may be implemented alternatively as step 251 and / or step 252 .

[0117] Step 251: When the number of extracted accounts of the ith ranking list is not greater than the number of sub-recommendations of the ith ranking list, a potential friend account that meets the adding condition is extracted from the ith ranking list and added to the friend recommendation list as a target friend account;

[0118] The i-th extracted number is the number of potential friend accounts in the i-th ranking list that have been successfully extracted to the friend recommendation list.

[0119] In some embodiments, when the number of potential friends successfully extracted into the friend recommendation list does not reach the sub-recommendation number corresponding to the i-th ranking list, potential friend accounts that meet the adding conditions are extracted from the i-th ranking list and added to the friend recommendation list as target friend accounts.

[0120] For example, there are 10 potential friend accounts in the i-th ranking list, the number of sub-recommendations in the i-th ranking list is 5, and the i-th extracted number is 3. Then, a potential friend account that meets the adding conditions is continuously extracted from the i-th ranking list and added to the friend recommendation list as the target friend account. At this time, the i-th extracted number becomes 4. A potential friend account that meets the adding conditions is continuously extracted from the i-th ranking list and added to the friend recommendation list as the target friend account. At this time, the i-th extracted number becomes 5.

[0121] Step 252: When the i-th extracted number is greater than the number of sub-recommendations of the i-th ranking list, a potential friend account that meets the adding condition is extracted from the i+1-th ranking list and added to the friend recommendation list as the target friend account.

[0122] In some embodiments, when the number of potential friends successfully extracted from the friend recommendation list reaches the sub-recommendation number corresponding to the i-th ranking list, potential friend accounts are no longer extracted from the i-th ranking list, and potential friend accounts that meet the adding conditions are extracted from the i+1-th ranking list and added to the friend recommendation list as target friend accounts.

[0123] In some embodiments, when the number of potential friends successfully extracted from the friend recommendation list reaches the number of sub-recommendations corresponding to the ith ranking list, the potential friend account of the ith ranking list is deleted.

[0124] For example, the i-th ranking list has 10 potential friend accounts, the number of sub-recommendations of the i-th ranking list is 5, and the i-th extracted number is 5. Then, a potential friend account that meets the adding conditions is continuously extracted from the i+1-th ranking list and added to the friend recommendation list as the target friend account.

[0125] In summary, the method provided in this embodiment implements the method of determining whether to continue to extract potential friend accounts from the ranking list based on whether the number of potential friends that have been successfully extracted from the ranking list to the friend recommendation list has reached the sub-recommendation number of the ranking list. If not, continue to extract from the ranking list; if it has reached, extract from the next ranking list. In this way, judging while extracting, once the sub-recommendation number is met, switch to the next ranking list. Compared with the method of extracting each potential account in the ranking list, it can effectively improve the efficiency of generating the friend recommendation list.

[0126] Based on Figure 6 In an optional embodiment, if Figure 7 As shown, step 251 can be replaced by at least one of steps 2511 to 2513.

[0127] Step 2511: If the number of extracted accounts of the ith ranking list is not greater than the number of sub-recommendations of the ith ranking list, extract the jth potential friend account from the ith ranking list, where j is a positive integer;

[0128] The i-th extracted number is the number of potential friend accounts in the i-th ranking list that have been successfully extracted to the friend recommendation list.

[0129] In some embodiments, when the number of potential friends successfully extracted from the friend recommendation list does not reach the number of sub-recommendations corresponding to the ith ranking list, the jth potential friend account is extracted from the ith ranking list.

[0130] Exemplarily, the i-th ranking list has 5 potential friend accounts, the number of sub-recommendations of the i-th ranking list is 3, and the i-th extracted number is 0, then the 1st potential friend account is extracted from the i-th ranking list; or, the 2nd potential friend account is extracted from the i-th ranking list; or, the 3rd potential friend account is extracted from the i-th ranking list; or, the 4th potential friend account is extracted from the i-th ranking list; or, the 5th potential friend account is extracted from the i-th ranking list.

[0131] Step 2512: If the jth potential friend account extracted does not overlap with a potential friend account in the friend recommendation list, add it to the friend recommendation list as a target friend account;

[0132] For example, Figure 8As shown, there are 3 rankings, namely, ranking A, ranking B and ranking C, and the corresponding sub-recommendation numbers are 5, 3, and 2, respectively. There are 10 potential friend accounts corresponding to ranking A, which are arranged in order from a1 to a10, there are 10 potential friend accounts corresponding to ranking B, which are arranged in order from a1 to a10, and there are 10 potential friend accounts corresponding to ranking C, which are arranged in order from a11, a1 to a8, and a12. At this time, the i-th ranking is ranking B, and a1 to a5 already exist in the friend recommendation list 20. At this time, j=6, and the 6th potential friend account extracted is a6, which is not repeated with all the potential friend accounts already in the friend recommendation list. The j-th potential friend is added as the target friend account to the position 21 of the friend recommendation list.

[0133] Step 2513: If the j-th potential friend account extracted is the same as a potential friend account in the friend recommendation list, set j=j+1 and execute the step of extracting the j-th potential friend account from the i-th ranking list again.

[0134] In some embodiments, if the j-th potential friend account extracted is the same as a potential friend account in the friend recommendation list, set j=j+1 and execute step 2511 again.

[0135] For example, Figure 8 As shown, there are 3 rankings, namely, ranking A, ranking B and ranking C, and the corresponding sub-recommendation numbers are 5, 3, and 2, respectively. There are 10 potential friend accounts corresponding to ranking A, which are arranged in order from a1 to a10, there are 10 potential friend accounts corresponding to ranking B, which are arranged in order from a1 to a10, and there are 10 potential friend accounts corresponding to ranking C, which are arranged in order from a11, a1 to a8, and a12. At this time, the i-th ranking is ranking B, and a1 to a5 already exist in the friend recommendation list 20. At this time, j=5, and the fifth potential friend account extracted is a5, which is repeated with the potential friend account a5 already in the friend recommendation list. Let j=6, and execute the step of extracting the sixth potential friend account from ranking B again.

[0136] In summary, the method provided in this embodiment extracts potential friend accounts one by one from the potential friend accounts corresponding to the ranking list, and selectively adds potential friend accounts by judging whether they are in the friend recommendation list. This ensures that the potential friend accounts in the friend recommendation list are not repeated, and can recommend potential friends to the user to the greatest extent.

[0137] Based on Figure 3 In an optional embodiment, if Fig. 9 As shown, step 220 can be implemented alternatively as step 211 and step 222 .

[0138] Step 221: Obtain game data of the current account for at least two rankings;

[0139] The game data of the current account for at least two rankings includes: at least one of the game play participation of each ranking and the number of ranking refreshes of each ranking.

[0140] In some embodiments, the number of ranking refreshes is the number of times the current account refreshes the ranking; the gameplay participation is the current account's participation in the gameplay of the ranking, that is, the length of time and / or number of times the player participates in the gameplay of the ranking.

[0141] In some embodiments, the gameplay engagement is the number of times the gameplay is played; or, the gameplay engagement is the length of time invested in the gameplay; or, the gameplay engagement is based on the length of time invested in the gameplay and the number of times the gameplay is played.

[0142] Exemplarily, the game platform has two rankings, the popularity ranking and the combat power ranking. The gameplay corresponding to the popularity ranking is changing costumes. The gameplay corresponding to the combat power ranking is changing equipment and upgrading equipment. The gameplay participation for the popularity ranking is 3 times of changing costumes; the gameplay participation for the combat power ranking is 3 times of changing equipment and 4 times of upgrading equipment; or, the gameplay participation for the popularity ranking is 10 hours invested in changing costumes; the gameplay participation for the combat power ranking is 5 hours invested in changing equipment and 10 hours invested in upgrading equipment; or, the gameplay participation for the popularity ranking is 3 times of changing costumes; the gameplay participation for the combat power ranking is 3 times of changing equipment and 8 hours invested in upgrading equipment.

[0143] Step 222: Based on the game data of the current account for at least two rankings, calculate the ranking participation percentage of the current account for at least two rankings.

[0144] In some embodiments, the ranking participation percentage is calculated based on the sum of the ranking refresh times and the gameplay participation; or, the ranking percentage is calculated based on the weighted sum of the ranking refresh times and the gameplay participation.

[0145] Exemplarily, the game program has three rankings, namely the popularity ranking, the rank ranking and the combat power ranking. The ranking refresh times of the popularity ranking is 3, and the corresponding gameplay participation of the popularity ranking is 7; the ranking refresh times of the rank ranking is 5, and the corresponding gameplay participation of the rank ranking is 10; the ranking refresh times of the combat power ranking is 3, and the corresponding gameplay participation of the combat power ranking is 2. The ranking participation ratio is calculated according to the sum of the ranking refresh times and the gameplay participation. First, the sum of the ranking refresh times of all rankings and the corresponding gameplay participation is calculated as 3+7+5+10+3+2, and the result is 30; then the sum of the ranking refresh times of each ranking and the corresponding gameplay participation is calculated separately. The sum of the ranking refresh times of the popularity ranking and the corresponding gameplay participation is 3+7, and the result is 10; the sum of the ranking refresh times of the rank ranking and the corresponding gameplay participation is 5+10, and the result is 15; the sum of the ranking refresh times of the combat power ranking and the corresponding gameplay participation is 3+2, and the result is 5. Finally, calculate the proportion of the sum of the number of ranking refreshes and the corresponding gameplay participation of each ranking to the sum of the number of ranking refreshes and the corresponding gameplay participation of all rankings, where the popularity ranking is 10 / 30, the result is 1 / 3; the rank ranking is 15 / 30, the result is 1 / 2; the combat power ranking is 5 / 30, the result is 1 / 6. Therefore, the ranking proportion corresponding to the popularity ranking is 1 / 3, the ranking proportion corresponding to the rank ranking is 1 / 2, and the ranking proportion corresponding to the combat power ranking is 1 / 6; or, calculate the ranking proportion based on the weighted sum of the number of ranking refreshes and the gameplay participation, the weight corresponding to the number of ranking refreshes is 0.2, and the weight corresponding to the gameplay participation is 0.8. First, calculate the weighted sum of the number of ranking refreshes and the corresponding gameplay participation of all rankings 3*0.2+7*0.8+5*0.2+10*0.8+3*0.2+2*0 .8, the result is 17.4; then calculate the sum of the number of ranking refreshes and the corresponding gameplay participation of each ranking list respectively. The weighted sum of the number of ranking refreshes and the corresponding gameplay participation of the heat ranking list is 3*0.2+7*0.8, the result is 6.2; the weighted sum of the number of ranking refreshes and the corresponding gameplay participation of the rank ranking list is 5*0.2+10*0.8, the result is 9; the weighted sum of the number of ranking refreshes and the corresponding gameplay participation of the combat power ranking list is 3*0.2+2*0.8, the result is 2.2. Finally, calculate the proportion of the sum of the number of ranking refreshes and the corresponding gameplay participation of each ranking list to the sum of the number of ranking refreshes and the corresponding gameplay participation of all ranking lists. Among them, the heat ranking list is 6.2 / 17.4, the result is 31 / 87; the rank ranking list is 9 / 17.4, the result is 45 / 87; the combat power ranking list is 2.2 / 17.4, the result is 11 / 87.Therefore, the ranking list corresponding to the popularity ranking accounts for 31 / 87, the ranking list corresponding to the rank ranking accounts for 45 / 87, and the ranking list corresponding to the combat power ranking accounts for 11 / 87.

[0146] In some embodiments, the leaderboard refresh times are normalized; or, the gameplay engagement is normalized; or, the leaderboard refresh times and the gameplay engagement are normalized. Normalization refers to a method of limiting data within a fixed range. Normalization makes data with different units of measurement comparable. For example, gameplay engagement can be measured based on time invested and number of engagements, but time and number of engagements are not comparable, so they need to be normalized to convert time invested and number of engagements into comparable data.

[0147] For example, the game play participation degree d1 is 10 hours invested in activity e1, and the game play participation degree d2 is 3 times participated in activity e2. Game play participation degrees d1 and d2 are normalized according to the set normalization method to make them comparable. Depending on the normalization method adopted, for example, the normalized d1 is 10 and the normalized d2 is 3; or, according to the participation of all users, the average user investment time for activity e1 is 2 hours, and the average number of user participations in activity e2 is 1 time, the normalized d1 is 5, and the normalized d2 is 3; or, according to the user participation, the average user investment time for all activities is 40 hours, and the average number of user participations in all activities is 1 time, the normalized d1 is 0.25, and the normalized d2 is 3. It should be noted that the present application is demonstrated using the unit removal method among the above-mentioned normalization methods, and the embodiments of the present application only list some normalization methods. The embodiments of the present application do not list the remaining normalization methods one by one, but the scope of protection of the present application is not limited to this.

[0148] Exemplarily, as shown in the above step 221, the normalized game play participation is calculated respectively. The gameplay participation for the popularity ranking list is 3 times of changing fashion operations, and the normalized gameplay participation for the popularity ranking list is 3; the gameplay participation for the combat power ranking list is 3 times of changing equipment and 4 times of upgrading equipment, and the normalized gameplay participation for the combat power ranking list is 7; or, the gameplay participation for the popularity ranking list is 10 hours invested in changing fashion, and the normalized gameplay participation for the popularity ranking list is 10; the gameplay participation for the combat power ranking list is 5 hours invested in changing equipment and 10 hours invested in upgrading equipment, and the normalized gameplay participation for the combat power ranking list is 15; or, the gameplay participation for the popularity ranking list is 3 times of changing fashion operations, and the normalized gameplay participation for the popularity ranking list is 3; the gameplay participation for the combat power ranking list is 3 times of changing equipment and 8 hours invested in upgrading equipment, and the normalized gameplay participation for the combat power ranking list is 11.

[0149] In some embodiments, for at least two gameplays in the leaderboard, the gameplay engagement corresponding to the leaderboard is the sum of the gameplay engagement of each gameplay; or, the gameplay engagement corresponding to the leaderboard is the weighted sum of the gameplay engagement of each gameplay.

[0150] Exemplarily, the gameplay corresponding to the battle power ranking list is changing equipment and upgrading equipment. The gameplay participation for the battle power ranking list is that the equipment was changed 3 times and 8 hours were invested in upgrading equipment. After normalization, the gameplay participation of the equipment replacement gameplay for the battle power ranking list is 3, and the gameplay participation of the equipment upgrading gameplay for the battle power ranking list is 8. Then the gameplay participation for the battle power ranking list is the sum of the gameplay participation of the two gameplays, 11; or, the weight of the equipment replacement gameplay is 0.6, and the weight of the equipment upgrading gameplay is 1.8, and the gameplay participation for the battle power ranking list is the weighted sum of the gameplay participation of the two gameplays, 16.2.

[0151] In some embodiments, the weight corresponding to each gameplay is a preset value; or, the weight corresponding to each gameplay is a value that changes dynamically according to the frequency of participation in the gameplay.

[0152] Exemplarily, the gameplay corresponding to the battle power ranking list is changing equipment and upgrading equipment. The weight of the gameplay of changing equipment is 0.6, and the weight of the gameplay of upgrading equipment is 1.8; or, the weight can be divided according to the frequency, and the settlement period can be set according to different gameplays, and the settlement period includes at least one of daily settlement, weekly settlement, and monthly settlement. For example, the weight of three times or more a day is 3, and the weight of less than three times a day is 1; the weight of ten times or more a week is 5, and the weight of less than ten times a week is 2; the weight of fifteen times or more a month is 8, and the weight of less than fifteen times a month is 3; the weight of one hour or more a day is 3, and the weight of less than one hour a day is 1; the weight of eight hours or more a week is 4, and the weight of less than eight hours a week is 2.5; the weight of twenty hours or more a month is 6, and the weight of less than twenty hours a month is 1; wherein, the weight corresponding to the number of participations and / or investment time of the gameplay can be determined according to the importance of the gameplay. It should be noted that the embodiment of the present application only lists the weight setting schemes corresponding to some game playing methods, and the weight setting schemes corresponding to the game playing methods are no longer listed one by one in this embodiment, but the protection scope of the present application is not limited to this.

[0153] To sum up, the method provided in this embodiment shows a method for calculating the ranking participation ratio of a ranking list. Reasonable calculations are performed based on the acquired game data to obtain the user's participation level in the gameplay corresponding to each ranking list, and the user's interest level in different ranking lists is extracted from the game data as much as possible, which makes it convenient to select potential friends in the ranking list that the user is interested in when making friend recommendations, thereby improving the quality of potential friends recommended by friends.

[0154] Based on Fig. 9 In an optional embodiment, if Fig.10 As shown, step 222 can be implemented alternatively as steps 2221 to 2223.

[0155] Step 2221: Calculate the total participation level based on the game data of the current account for at least two rankings, where the total participation level is used to indicate the participation level of the game play of the at least two rankings in which the current account participates;

[0156] The game data of the current account for at least two rankings includes: at least one of the game play participation of each ranking and the number of ranking refreshes of each ranking.

[0157] For example, the game program has three rankings, namely the popularity ranking, the rank ranking and the combat power ranking. The number of ranking refreshes of the popularity ranking is 3, and the corresponding gameplay participation of the popularity ranking is 7; the number of ranking refreshes of the rank ranking is 5, and the corresponding gameplay participation of the rank ranking is 10; the number of ranking refreshes of the combat power ranking is 3, and the corresponding gameplay participation of the combat power ranking is 2. The ranking participation ratio is calculated based on the sum of the ranking refreshes and the gameplay participation. First, the sum of the ranking refreshes and the corresponding gameplay participation of all rankings is calculated, which is 3+7+5+10+3+2, and the result is 30.

[0158] Step 2222: Calculate the participation level of the current account in the gameplay of the ranking list based on the game data of the current account for each ranking list;

[0159] Exemplarily, as shown in the above step 2221, the sum of the number of ranking refreshes and the corresponding gameplay participation of each ranking list is calculated respectively. The sum of the number of ranking refreshes and the corresponding gameplay participation of the popularity ranking list is 3+7, which is 10; the sum of the number of ranking refreshes and the corresponding gameplay participation of the rank ranking list is 5+10, which is 15; the sum of the number of ranking refreshes and the corresponding gameplay participation of the combat power ranking list is 3+2, which is 5.

[0160] In some embodiments, based on the game data of the current account for each leaderboard and the weight corresponding to the game data, the participation level of the current account in the gameplay of the leaderboard is calculated.

[0161] Exemplarily, the game data includes the number of ranking refreshes and the degree of game play participation. The weight corresponding to the number of ranking refreshes is 0.2, and the weight corresponding to the degree of game play participation is 0.8. The weighted sum of the number of ranking refreshes and the corresponding degree of game play participation of all rankings is calculated as 3*0.2+7*0.8+5*0.2+10*0.8+3*0.2+2*0.8, and the result is 17.4; the sum of the number of ranking refreshes and the corresponding degree of game play participation of each ranking is calculated separately, and the weighted sum of the number of ranking refreshes and the corresponding degree of game play participation of the popularity ranking is 3*0.2+7*0.8, and the result is 6.2; the weighted sum of the number of ranking refreshes and the corresponding degree of game play participation of the rank ranking is 5*0.2+10*0.8, and the result is 9; the weighted sum of the number of ranking refreshes and the corresponding degree of game play participation of the combat power ranking is 3*0.2+2*0.8, and the result is 2.2. Finally, calculate the ratio of the sum of the number of ranking refreshes and the corresponding gameplay participation of each ranking to the sum of the number of ranking refreshes and the corresponding gameplay participation of all rankings. The heat ranking is 6.2 / 17.4, and the result is 31 / 87; the rank ranking is 9 / 17.4, and the result is 45 / 87; the combat power ranking is 2.2 / 17.4, and the result is 11 / 87. Therefore, the ranking ratio corresponding to the heat ranking is 31 / 87, the ranking ratio corresponding to the rank ranking is 45 / 87, and the ranking ratio corresponding to the combat power ranking is 11 / 87.

[0162] Step 2223: Calculate the proportion of the current account's participation in the gameplay of each ranking list to the total participation.

[0163] Exemplarily, as shown in the above step 2222, finally, the sum of the number of ranking refreshes and the corresponding game play participation of each ranking is calculated as the proportion of the sum of the number of ranking refreshes and the corresponding game play participation of all rankings, where the popularity ranking is 10 / 30, the result is 1 / 3; the rank ranking is 15 / 30, the result is 1 / 2; the combat power ranking is 5 / 30, the result is 1 / 6. Therefore, the ranking corresponding to the popularity ranking accounts for 1 / 3, the ranking corresponding to the rank ranking accounts for 1 / 2, and the ranking corresponding to the combat power ranking accounts for 1 / 6.

[0164] In some embodiments, step 2221 and step 2222 may be performed in an interchangeable order or simultaneously.

[0165] In summary, the method provided in this embodiment shows a method for calculating the ranking participation ratio. According to the actual operation requirements of the game platform, the weight of each game data can be specifically designed to calculate the ranking participation ratio. The ranking participation ratio calculated in this way can better meet the design requirements of the game platform.

[0166] Based on Figure 3 In an optional embodiment, if Fig.11 As shown, step 230 can be implemented alternatively as step 231 .

[0167] Step 231: Obtain potential friend accounts of the current account that meet a first quantity in each ranking list, where the first quantity is not less than a total recommendation quantity, and the total recommendation quantity is the capacity of the friend recommendation list.

[0168] Exemplarily, a request is made to obtain potential friend accounts no less than the capacity of the friend recommendation list from each ranking list to prevent the potential friend accounts obtained from each ranking list from having a high degree of duplication, resulting in the number of potential friends obtained in one request failing to meet the capacity requirement of the friend recommendation list, and resulting in the need to obtain the potential friend accounts of the ranking list multiple times.

[0169] In summary, the method provided in this embodiment obtains a number of potential friend accounts that is not less than the capacity of the friend recommendation list when requesting to obtain potential friend accounts for each ranking list, thereby preventing the need to request the potential friend accounts from the ranking list multiple times due to a high degree of duplication of potential friend accounts among multiple ranking lists, thereby reducing server overhead and also increasing the speed of recommending friend accounts.

[0170] Based on Fig.11 In an optional embodiment, if Fig.12 As shown, step 231 can be implemented alternatively as steps 2311 to 2313.

[0171] Step 2311: Obtain the ranking of the current account for at least two rankings;

[0172] In some embodiments, the ranking of the current account in each ranking is obtained.

[0173] For example, there are three rankings, namely, the popularity ranking, the rank ranking and the combat power ranking. The ranking of the popularity ranking of the current account is 40, the ranking of the rank ranking is 20, and the ranking of the combat power ranking is 5.

[0174] Step 2312: according to the ranking of each ranking list, obtaining a potential friend account that meets a first condition from each ranking list, the first condition being that the ranking of the ranking list corresponding to the potential friend account is greater than the ranking of the ranking list;

[0175] In some embodiments, according to the ranking of each ranking list, a potential friend account is obtained from each ranking list, and the ranking of the obtained potential friend account in the ranking list is greater than the ranking of the current account in the ranking list.

[0176] In some embodiments, the smaller the number corresponding to the leaderboard ranking, the larger the leaderboard ranking.

[0177] For example, in the popularity ranking list, the ranking of the current account is 40, and at least one of the potential friend accounts ranked 1 to 39 in the popularity ranking list is obtained. For example, the potential friend account ranked 39 in the popularity ranking list is obtained; or, the potential friend accounts ranked 20 to 39 in the popularity ranking list are obtained; or, the potential friend accounts ranked 20 to 30 in the popularity ranking list are obtained.

[0178] Step 2313: According to the ranking of each ranking list, obtain the potential friend account that meets the second condition from each ranking list, the second condition being that the ranking of the potential friend account corresponding to the ranking list is less than the ranking of the ranking list.

[0179] The first number is the sum of the number of potential friend accounts that meet the first condition and the number of potential friend accounts that meet the second condition.

[0180] In some embodiments, a potential friend account is obtained from each ranking list according to the ranking list of each ranking list, and the ranking list ranking of the obtained potential friend account in the ranking list is smaller than the ranking list ranking of the current account in the ranking list.

[0181] For example, in the popularity ranking list, the ranking of the current account is 40, and at least one of the potential friend accounts from the ranking 41 to the minimum ranking in the popularity ranking list is obtained. For example, the potential friend account with a ranking of 41 in the popularity ranking list is obtained; or, the potential friend accounts with rankings of 41 to 50 in the popularity ranking list are obtained; or, the potential friend accounts with rankings of 50 to 100 in the popularity ranking list are obtained.

[0182] In some embodiments, the number of potential friend accounts that meet the first condition and the number of potential friend accounts that meet the second condition obtained are greater than the total number of recommendations in the friend recommendation list.

[0183] In some embodiments, step 2312 and step 2313 may be performed in an interchangeable order or simultaneously.

[0184] In summary, the method provided in this embodiment shows a method for obtaining potential friend accounts when recommending friend accounts. According to the ranking of the current account, potential friend accounts with similar rankings are recommended to the current account. Similar rankings in the rankings mean that the selected potential friend accounts have a similar degree of participation in the rankings as the current account, and the quality of the potential friend accounts is high.

[0185] In some embodiments, when executing the friend account recommendation method, the server will call two types of processes, namely lobby process and ranking process. The lobby process is responsible for managing game accounts and can query the corresponding game data of game accounts. Each ranking in the game platform corresponds to a ranking process, which is used to obtain the data corresponding to the corresponding ranking.

[0186] The following will explain the method of recommending friend accounts based on the lobby process and ranking process in the server, with at least two rankings being the popularity ranking, the rank ranking and the combat power ranking.

[0187] Fig.13 A flowchart of a friend account recommendation scheme provided by an exemplary embodiment of the present application is shown. The method includes steps 310 to 326.

[0188] Step 310: The terminal device sends a request to the server;

[0189] In some embodiments, in response to a trigger operation on a friend recommendation control, the terminal device sends a request to the server, where the request is used to request the server to obtain a friend recommendation list.

[0190] In some embodiments, the request sent by the terminal device to the server includes the current account.

[0191] Step 311: The lobby process obtains the current account;

[0192] In some embodiments, the lobby process obtains the current account in the request; or, the lobby process requests the terminal device to obtain the current account; or, the lobby process obtains the current account based on the terminal device number query.

[0193] Exemplarily, the lobby process queries the most recently logged-in account information corresponding to the terminal device according to the terminal device number as the current account; or, the lobby process queries the currently online account information corresponding to the terminal device according to the terminal device number as the current account.

[0194] Step 312: The lobby process determines whether the current account is on the popularity ranking list;

[0195] In some embodiments, the popularity ranking list is used to indicate the popularity ranking of all user accounts in the game platform; or, the popularity ranking list is used to indicate the popularity ranking of user accounts within a ranking threshold in the game platform.

[0196] In some embodiments, the ranking threshold is a set threshold.

[0197] Exemplarily, the popularity ranking list is used to indicate the popularity ranking of all user accounts in the game platform; or, the popularity ranking list is used to indicate the popularity ranking of the top 100,000 user accounts in the game platform.

[0198] In some embodiments, if the current account is on the popularity ranking list, a request is made to the popularity ranking process to obtain potential friend accounts in the popularity ranking list; if the current account is not on the popularity ranking list, step 315 is executed.

[0199] Step 313: The lobby process determines whether the current account is on the rank ranking list;

[0200] In some embodiments, the rank ranking list is used to indicate the rank rankings of all user accounts in the game platform; or, the rank ranking list is used to indicate the rank rankings of user accounts within a ranking threshold in the game platform.

[0201] In some embodiments, the ranking threshold is a set threshold.

[0202] Exemplarily, the rank ranking list is used to indicate the rank rankings of all user accounts in the game platform; or, the rank ranking list is used to indicate the rank rankings of the top 100,000 user accounts in the game platform.

[0203] In some embodiments, if the current account is on the rank ranking list, a request is made to the rank ranking process to obtain potential friend accounts in the rank ranking list; if the current account is not on the rank ranking list, step 315 is executed.

[0204] Step 314: The lobby process determines whether the current account is on the battle power ranking list;

[0205] In some embodiments, the battle power ranking list is used to indicate the battle power ranking of all user accounts in the game platform; or, the battle power ranking list is used to indicate the battle power ranking of user accounts within a ranking threshold in the game platform.

[0206] In some embodiments, the ranking threshold is a set threshold.

[0207] Exemplarily, the battle power ranking list is used to indicate the battle power rankings of all user accounts in the game platform; or, the battle power ranking list is used to indicate the battle power rankings of the top 100,000 user accounts in the game platform.

[0208] In some embodiments, if the current account is on the battle power ranking list, a request is made to the battle power ranking list process to obtain potential friend accounts in the battle power ranking list; if the current account is not on the battle power ranking list, step 315 is executed.

[0209] In some embodiments, the above steps 312, 313 and 314 may be executed in a swapped order or simultaneously.

[0210] It should be noted that in the above steps 312 to 314, the request to obtain the potential friend account from the ranking process may be initiated synchronously or asynchronously, that is, the above three requests may be issued simultaneously or separately.

[0211] Step 315: the lobby process waits for the ranking process to return data;

[0212] In some embodiments, the lobby process waits for any leaderboard process to return a potential friend account.

[0213] Step 316: any ranking process returns data asynchronously;

[0214] In some embodiments, due to the different amounts of data stored in each ranking list, the different numbers of potential friends that need to be obtained, and the different rankings of the ranking list, the ranking list process returns the potential friend data at different times.

[0215] Exemplarily, the data in the ranking list is stored in separate tables, and the data of every 100,000 accounts is stored in one data table, that is, the data of accounts ranked 1 to 100,000 on the ranking list is stored in one data table, and the data of accounts ranked 100,000 to 200,000 on the ranking list is stored in one data table, and so on. The ranking process queries potential friend accounts in a sequential manner, that is, starting from the account data ranked 1 on the ranking list; or, the ranking process queries potential friend accounts based on an index search, and the index is the position in each data table, for example, the account data ranked 1 on the ranking list is stored in the first position of the first table. Therefore, depending on the different storage methods and search methods, the time required for each request to search for potential friend accounts is different, and the time to return data is naturally different.

[0216] Step 317: Activate the friend recommendation system in the lobby process;

[0217] In some embodiments, the lobby process is responsible for managing game accounts and can query the corresponding game data of the game accounts. The lobby process can be divided into at least one system according to different functions implemented. For example, the lobby process includes a friend recommendation system for executing a friend account recommendation method.

[0218] Step 318: The lobby process determines whether there is any data that has not been returned;

[0219] In some embodiments, the lobby process waits for all leaderboard processes to return potential friend accounts.

[0220] For example, the current account is on the popularity ranking list and the combat power ranking list. The lobby process initiates a request to obtain potential friend accounts from the popularity ranking process and the combat power ranking process, and saves the request information in the server. Each time data returned by a ranking process is received, it is determined whether all ranking processes have returned data.

[0221] In some embodiments, if a ranking process does not return data within the response time, the lobby process again initiates a request to obtain potential friend accounts from the ranking process that has not returned data.

[0222] In some embodiments, the lobby process packages the potential friend accounts returned by each leaderboard process into a potential friend account set; or, the lobby process obtains the potential friend account set returned by each leaderboard process.

[0223] Step 319: The lobby process calculates the ranking participation percentages of at least two rankings;

[0224] In some embodiments, the lobby process calculates the leaderboard participation percentages of at least two leaderboards that the current account has been on, and the leaderboard participation percentage of each leaderboard is used to indicate the level of participation of the current account in participating in the gameplay of the leaderboard.

[0225] In some embodiments, a leaderboard corresponds to at least one gameplay.

[0226] For example, the game platform has three rankings, namely, the popularity ranking, the rank ranking, and the combat power ranking. The gameplay corresponding to the popularity ranking is to change the costume. The gameplay corresponding to the rank ranking is to participate in the ranking competition. The gameplay corresponding to the combat power ranking is to change equipment and upgrade equipment.

[0227] Step 320: The lobby process calculates the number of sub-recommendations of at least two rankings;

[0228] For example, there are three rankings, and the current account's ranking participation ratio for each ranking is 0.2, 0.3, and 0.5 respectively. According to the current account's ranking participation ratio for each ranking, the number of sub-recommendations for each ranking is determined to be 2, 3, and 5 respectively; or, according to the current account's ranking participation ratio for each ranking, the number of sub-recommendations for each ranking is determined to be 4, 6, and 10 respectively; or, according to the current account's ranking participation ratio for each ranking, the number of sub-recommendations for each ranking is determined to be 6, 9, and 15 respectively.

[0229] Step 321: The lobby process selects a potential friend account set according to the participation ratio of the ranking list;

[0230] In some embodiments, a leaderboard has a set of potential friend accounts.

[0231] In some embodiments, the lobby process sorts the potential friend account sets corresponding to the ranking list according to the participation ratio of the ranking list.

[0232] In some embodiments, the potential friend account set includes at least one potential friend account.

[0233] Exemplarily, there are three rankings, namely Ranking A, Ranking B and Ranking C. Get the 10 potential friend accounts corresponding to the current account in Ranking A, get the 10 potential friend accounts corresponding to the current account in Ranking B, and get the 8 potential friend accounts corresponding to the current account in Ranking C; or, get the 2 potential friend accounts corresponding to the current account in Ranking A, get the 3 potential friend accounts corresponding to the current account in Ranking B, and get the 20 potential friend accounts corresponding to the current account in Ranking C; or, get the 10 potential friend accounts corresponding to the current account in Ranking A, get the 10 potential friend accounts corresponding to the current account in Ranking B, and get the 10 potential friend accounts corresponding to the current account in Ranking C.

[0234] In some embodiments, the potential friend account includes at least one of: a potential friend account number, a potential friend account name, and an online status of the potential friend account.

[0235] Step 322: the lobby process selects a potential friend account from the potential friend account set;

[0236] In some embodiments, the potential friend accounts are stored in the potential friend account set in a certain order.

[0237] In some embodiments, potential friend accounts are saved in the potential friend account set based on ranking in descending order; or, potential friend accounts are saved in the potential friend account set based on ranking in descending order; or, potential friend accounts are saved in the potential friend account set based on the difference between ranking in the ranking and ranking corresponding to the current account; or, potential friend accounts are saved in the potential friend account set based on the difference between ranking in the ranking and ranking corresponding to the current account.

[0238] Exemplarily, there are 4 potential friend accounts a1 to a4 in the potential friend account set, and their corresponding rankings are 10, 11, 13, and 14 respectively. The ranking corresponding to the current account is 12. The order in the potential friend account set is saved from large to small according to the ranking, that is, [10, 11, 13, 14]; or, the order in the potential friend account set is saved from small to large according to the ranking, that is, [14, 13, 11, 10]; or, the order in the potential friend account set is saved from small to large according to the difference between the ranking and the ranking corresponding to the current account, that is, [11, 13, 10, 14], or [13, 11, 14, 10]; or, the order in the potential friend account set is saved from large to small according to the difference between the ranking and the ranking corresponding to the current account, that is, [10, 14, 11, 13], or [14, 10, 13, 11].

[0239] Step 323: The lobby process determines whether the sub-recommendation number of the potential friend account set has been reached;

[0240] In some embodiments, the lobby process determines whether the number of potential friend accounts in the potential friend account set that have been successfully added to the friend recommendation list has reached the number of sub-recommendations of the corresponding ranking list of the potential friend account set. If so, the next potential friend account set is selected and step 321 is executed; if not, step 324 is continued.

[0241] Step 324: The lobby process determines whether the friend recommendation list is full;

[0242] In some embodiments, the lobby process determines whether the friend recommendation list is full. If the friend recommendation list is full, the method ends and the friend recommendation list is returned to the terminal device; if the friend recommendation list is not full, step 325 is continued.

[0243] Step 325: The lobby process determines whether the potential friend account is the same as the account in the friend recommendation list;

[0244] In some embodiments, the lobby process determines whether the potential friend account is a duplicate of an account in the friend recommendation list. If so, step 322 is executed to retrieve the next potential friend account from the potential friend account set; if not, step 326 is executed.

[0245] Step 326: The lobby process adds the potential friend account to the friend recommendation list.

[0246] In summary, the method provided in this embodiment utilizes multiple processes to execute the above-mentioned friend account recommendation method, designs more details of process scheduling during the execution of the friend account recommendation method, so that the friend account recommendation method can be better implemented.

[0247] The following are device embodiments of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0248] Please refer to Figures 14 to 17 , which shows a structural block diagram of a friend account recommendation device provided by an exemplary embodiment of the present application. The device has the function of implementing the above-mentioned friend account recommendation method example, and the function can be implemented by hardware or by hardware executing corresponding software. Fig.14 As shown, the device 400 may include: a first acquisition module 410 , a calculation module 420 , a second acquisition module 430 , a determination module 440 and a generation module 450 .

[0249] A first acquisition module 410 is used to acquire a current account, where the current account participates in a game play having at least two leaderboards in the game platform;

[0250] A calculation module 420 is used to calculate the ranking participation ratio of the current account for the at least two rankings, the ranking participation ratio of each ranking is used to indicate the ratio of the participation level of the game play for the ranking to the total participation level, and the total participation level is used to indicate the participation level of the game play of the at least two rankings in which the current account participates;

[0251] The second acquisition module 430 is used to acquire potential friend accounts corresponding to the current account in the at least two rankings respectively;

[0252] A determination module 440 is used to determine the number of sub-recommendations for each of the rankings according to the ranking participation ratio of the current account for each of the rankings, wherein the number of sub-recommendations is used to indicate the number of the potential friend accounts that need to be extracted from the rankings;

[0253] The generating module 450 is configured to extract target friend accounts from the potential friend accounts respectively corresponding to the at least two ranking lists based on the number of sub-recommendations of each ranking list, and generate a friend recommendation list.

[0254] In some embodiments, Fig.15 As shown, the determination module 440 further includes: a first acquisition submodule 441 and a first determination submodule 442 .

[0255] The first acquisition submodule 441 is used to acquire the total number of recommendations in the friend recommendation list;

[0256] The first determination submodule 442 is used to determine the number of sub-recommendations for each of the rankings according to the ranking participation ratio of the current account for each of the rankings and the total number of recommendations.

[0257] In some embodiments, the first determining submodule includes a first determining unit.

[0258] The first determining unit is configured to determine the number of sub-recommendations for each of the rankings according to the product of the current account's ranking participation ratio for each of the rankings and the total number of recommendations.

[0259] In some embodiments, the generation module 450 includes a first extraction submodule and a second extraction submodule.

[0260] A first extraction submodule is used to extract a potential friend account that meets the adding condition from the i-th ranking list when the i-th extracted number is not greater than the sub-recommendation number of the i-th ranking list, and add it to the friend recommendation list as the target friend account;

[0261] A second extraction submodule is used for extracting a potential friend account that meets the adding condition from the (i+1)th ranking list when the i-th extracted number is greater than the sub-recommendation number of the i-th ranking list, and adding the potential friend account as the target friend account to the friend recommendation list;

[0262] In some embodiments, the first extraction submodule includes an extraction unit, an addition unit, and a repeated extraction unit.

[0263] an extracting unit, configured to extract a jth potential friend account from the ith ranking list, where j is a positive integer, when the ith extracted number is not greater than the sub-recommendation number of the ith ranking list;

[0264] An adding unit, configured to add the extracted j-th potential friend account to the friend recommendation list as the target friend account if the extracted j-th potential friend account does not overlap with a potential friend account in the friend recommendation list;

[0265] The repeated extraction unit is used for, if the j-th potential friend account extracted is the same as a potential friend account in the friend recommendation list, setting j=j+1 and executing the step of extracting the j-th potential friend account from the i-th ranking list again.

[0266] In some embodiments, Fig.16 As shown, the calculation module 420 includes a second acquisition submodule 421 and a calculation submodule 422 .

[0267] The second acquisition submodule 421 is used to acquire the game data of the current account for the at least two rankings;

[0268] A calculation submodule 422, configured to calculate a ranking participation ratio of the current account for the at least two rankings based on the game data of the current account for the at least two rankings;

[0269] In some embodiments, the computing submodule 422 includes a first computing unit, a second computing unit, and a third computing unit.

[0270] A first calculation unit, configured to calculate a total participation degree based on the game data of the current account for the at least two rankings, wherein the total participation degree is used to indicate a participation degree of the current account in the game play of the at least two rankings;

[0271] A second calculation unit, configured to calculate the participation degree of the current account in the game play of the ranking list based on the game data of the current account for each ranking list;

[0272] The third calculation unit is used to calculate the proportion of the current account's participation in the game play of each of the rankings to the total participation.

[0273] In some embodiments, the second computing unit includes a fourth computing unit.

[0274] A fourth calculation unit is used to calculate the participation degree of the current account in the game play of the ranking list based on the game data of the current account for each ranking list and the weight corresponding to the game data

[0275] In some embodiments, Fig.17 As shown, the second acquisition module 430 includes a third acquisition submodule 431 .

[0276] The third acquisition submodule 431 is used to acquire the potential friend accounts of the current account that meet a first quantity in each of the rankings, where the first quantity is greater than a total recommendation quantity, and the total recommendation quantity is the capacity of the friend recommendation list.

[0277] In some embodiments, the third acquisition submodule includes a first acquisition unit, a second acquisition unit, and a third acquisition unit.

[0278] A first obtaining unit, configured to obtain a ranking of the current account for the at least two rankings;

[0279] A second obtaining unit is used to obtain the potential friend account that meets a first condition from each of the rankings according to the ranking of each of the rankings, wherein the first condition is that the ranking of the ranking corresponding to the potential friend account is greater than the ranking of the ranking of the ranking;

[0280] The third acquisition unit is used to acquire the potential friend account that meets the second condition from each of the rankings according to the ranking of each of the rankings, wherein the second condition is that the ranking corresponding to the potential friend account is smaller than the ranking of the ranking.

[0281] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0282] Please refer to Fig.18 , which shows a structural block diagram of a server provided in an embodiment of the present application. The server can be used to implement the friend account recommendation method provided in the above embodiment. Specifically:

[0283] The server 500 includes a central processing unit (CPU) 501, a system memory 504 including a random access memory (RAM) 502 and a read-only memory (ROM) 503, and a system bus 505 connecting the system memory 504 and the central processing unit 501. The computer device 500 also includes a basic input / output system (I / O system) 506 for facilitating information transmission between various components in the computer device, and a large-capacity storage device 507 for storing an operating system 513, application programs 514, and other program modules 515.

[0284] The basic input / output system 506 includes a display 508 for displaying information and an input device 509 such as a mouse and a keyboard for user inputting information. The display 508 and the input device 509 are connected to the central processing unit 501 through an input / output controller 510 connected to the system bus 505. The basic input / output system 506 may also include an input / output controller 510 for receiving and processing inputs from a plurality of other devices such as a keyboard, a mouse, or an electronic stylus. Similarly, the input / output controller 510 also provides output to a display screen, a printer, or other types of output devices.

[0285] The mass storage device 507 is connected to the central processing unit 501 via a mass storage controller (not shown) connected to the system bus 505. The mass storage device 507 and its associated computer-readable storage medium provide non-volatile storage for the server 500. That is, the mass storage device 507 may include a computer-readable storage medium (not shown) such as a hard disk or a compact disc read-only memory (CD-ROM) drive.

[0286] Without loss of generality, the computer-readable storage medium may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable storage instructions, data structures, program modules or other data. Computer storage media include RAM, ROM, Erasable Programmable Read Only Memory (EPROM), Electronically-Erasable Programmable Read-Only Memory (EEPROM), flash memory or other solid-state storage technology, CD-ROM, Digital Versatile Disc (DVD) or other optical storage, cassette, tape, disk storage or other magnetic storage devices. Of course, those skilled in the art will know that the computer storage medium is not limited to the above. The above-mentioned system memory 504 and mass storage device 507 can be collectively referred to as memory.

[0287] The memory stores one or more programs, and the one or more programs are configured to be executed by one or more central processing units 501. The one or more programs contain instructions for implementing the above-mentioned method embodiments. The central processing unit 501 executes the one or more programs to implement the methods provided by the above-mentioned method embodiments.

[0288] According to various embodiments of the present application, the server 500 can also be connected to a remote server on the network through a network such as the Internet. That is, the server 500 can be connected to the network 512 through a network interface unit 511 connected to the system bus 505, or the network interface unit 511 can be used to connect to other types of networks or remote server systems (not shown).

[0289] The memory also includes one or more programs, which are stored in the memory and include steps executed by the server in the method provided in the embodiment of the present application.

[0290] In an exemplary embodiment, a non-transitory computer-readable storage medium is also provided, on which a computer program is stored, and when the computer program is executed by a processor, the method for recommending a friend account is implemented.

[0291] In an exemplary embodiment, a computer program product is also provided. When the computer program product is executed by a processor, it is used to implement the above-mentioned friend account recommendation method.

[0292] It should be understood that the "multiple" mentioned in this article refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. In addition, the step numbers described in this article only illustrate a possible execution sequence between the steps. In some other embodiments, the above steps may not be executed in the order of the numbers, such as two steps with different numbers are executed at the same time, or two steps with different numbers are executed in the opposite order to the diagram. The embodiments of the present application are not limited to this.

[0293] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A friend account recommendation method, characterized in that: The method comprises: Obtaining a current account, wherein the current account participates in a game having at least two leaderboards on a game platform; Calculating the ranking participation ratio of the current account for the at least two rankings, the ranking participation ratio of each ranking is used to indicate the ratio of the current account's participation level in the gameplay of the ranking to the total participation level, the total participation level is used to indicate the participation level of the current account in the gameplay of the at least two rankings; Obtaining potential friend accounts corresponding to the current account in the at least two ranking lists respectively; Determine the number of sub-recommendations for each ranking list according to the ranking list participation ratio of the current account for each ranking list, wherein the number of sub-recommendations is used to indicate the number of potential friend accounts that need to be extracted from the ranking list; Based on the number of sub-recommendations of each of the ranking lists, target friend accounts are extracted from the potential friend accounts respectively corresponding to the at least two ranking lists to generate a friend recommendation list.

2. The method according to claim 1, characterized in that The determining the number of sub-recommendations for each ranking list according to the ranking list participation ratio of the current account for each ranking list includes: Get the total number of recommendations in the friend recommendation list; The number of sub-recommendations for each ranking list is determined according to the ranking list participation ratio of the current account for each ranking list and the total number of recommendations.

3. The method according to claim 2, characterized in that The determining the number of sub-recommendations for each ranking list according to the ranking list participation ratio of the current account for each ranking list and the total number of recommendations includes: The number of sub-recommendations for each ranking list is determined according to the product of the ranking list participation ratio of the current account for each ranking list and the total number of recommendations.

4. The method according to any one of claims 1 to 3, characterized in that: The at least two rankings include the i-th ranking and the i+1-th ranking, where i is a positive integer; The step of extracting a target friend account from the potential friend accounts respectively corresponding to the at least two ranking lists based on the number of sub-recommendations of each ranking list to generate a friend recommendation list includes: In the case where the i-th extracted number is not greater than the sub-recommendation number of the i-th ranking list, extracting one of the potential friend accounts that meets the adding condition from the i-th ranking list and adding it to the friend recommendation list as the target friend account; In the case where the i-th extracted number is greater than the sub-recommendation number of the i-th ranking list, extracting one of the potential friend accounts that meets the adding condition from the i+1-th ranking list and adding it to the friend recommendation list as the target friend account; The i-th extracted number is the number of potential friend accounts in the i-th ranking list that have been successfully extracted to the friend recommendation list.

5. The method according to claim 4, characterized in that In the case where the i-th extracted number is not greater than the sub-recommendation number of the i-th ranking list, extracting a potential friend account that meets the adding condition from the i-th ranking list and adding it as the target friend account to the friend recommendation list includes: In the case where the i-th extracted number is not greater than the sub-recommendation number of the i-th ranking list, extracting the j-th potential friend account from the i-th ranking list, where j is a positive integer; If the j-th potential friend account extracted does not overlap with a potential friend account in the friend recommendation list, it is added to the friend recommendation list as the target friend account; If the j-th potential friend account extracted is the same as a potential friend account in the friend recommendation list, set j=j+1 and execute the step of extracting the j-th potential friend account from the i-th ranking list again.

6. The method according to any one of claims 1 to 5, characterized in that: The calculating the ranking participation ratio of the current account for the at least two rankings includes: Obtaining game data of the current account for the at least two rankings; Calculating, based on the game data of the current account for the at least two rankings, a ranking participation ratio of the current account for the at least two rankings; The game data of the current account for the at least two rankings include: at least one of the game play participation of each ranking and the number of ranking refreshes of each ranking.

7. The method according to claim 6, characterized in that The calculating, based on the game data of the current account for the at least two rankings, the ranking participation ratio of the current account for the at least two rankings comprises: Calculating the total participation level based on the game data of the current account for the at least two rankings, the total participation level being used to indicate the participation level of the current account in the game play of the at least two rankings; Calculating the participation level of the current account in the game play of each of the rankings based on the game data of the current account; The proportion of the current account's participation level in the game play of each of the rankings to the total participation level is calculated.

8. The method according to claim 7, characterized in that The calculating the participation level of the current account in the game play of each of the rankings based on the game data of the current account includes: Based on the game data of the current account for each of the rankings and the weight corresponding to the game data, the participation degree of the current account in the gameplay of the rankings is calculated.

9. The method according to any one of claims 1 to 5, characterized in that: The obtaining of potential friend accounts corresponding to the current account in the at least two rankings respectively includes: The potential friend accounts of the current account that meet a first quantity in each of the rankings are obtained, where the first quantity is not less than a total recommended quantity, and the total recommended quantity is a capacity of the friend recommendation list.

10. The method according to claim 9, characterized in that The obtaining of the potential friend accounts of the current account that meet a first number in each of the rankings includes: Obtaining a ranking of the current account for the at least two rankings; According to the ranking of each ranking list, obtaining the potential friend account that meets the first condition from each ranking list, wherein the first condition is that the ranking of the ranking list corresponding to the potential friend account is greater than the ranking of the ranking list; According to the ranking of each ranking list, the potential friend account that meets the second condition is obtained from each ranking list, wherein the second condition is that the ranking of the ranking list corresponding to the potential friend account is less than the ranking of the ranking list; The first number is the sum of the number of the potential friend accounts that meet the first condition and the number of the potential friend accounts that meet the second condition.

11. A device for recommending friend accounts, characterized in that: The device comprises: A first acquisition module is used to acquire a current account, where the current account participates in a game having at least two rankings in the game platform; a calculation module, configured to calculate the ranking participation ratio of the current account for the at least two rankings, wherein the ranking participation ratio of each ranking is used to indicate the ratio of the current account's participation in the gameplay of the ranking to the total participation ratio, and the total participation ratio is used to indicate the participation ratio of the current account in the gameplay of the at least two rankings; A second acquisition module is used to acquire potential friend accounts corresponding to the current account in the at least two rankings respectively; A determination module, configured to determine the number of sub-recommendations for each of the rankings according to the current account's ranking participation ratio for each of the rankings, wherein the number of sub-recommendations is used to indicate the number of potential friend accounts that need to be extracted from the rankings; The generating module is used to extract target friend accounts from the potential friend accounts respectively corresponding to the at least two ranking lists based on the number of sub-recommendations of each ranking list, and generate a friend recommendation list.

12. The device according to claim 11, characterized in that The determination module comprises: The first acquisition submodule is used to obtain the total number of recommendations in the friend recommendation list; The first determination submodule is used to determine the number of sub-recommendations for each of the rankings according to the ranking participation ratio of the current account for each of the rankings and the total number of recommendations.

13. The device according to claim 11 or 12, characterized in that The calculation module comprises: A second acquisition submodule is used to acquire game data of the current account for the at least two rankings; The calculation submodule is used to calculate the ranking participation ratio of the current account for the at least two rankings based on the game data of the current account for the at least two rankings.

14. The device according to claim 11 or 12, characterized in that The second acquisition module includes: The third acquisition submodule is used to acquire the potential friend accounts of the current account that meet a first quantity in each of the rankings, where the first quantity is not less than a total recommendation quantity, and the total recommendation quantity is the capacity of the friend recommendation list.

15. A computer device, characterized in that: The computer device includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the friend account recommendation method according to any one of claims 1 to 10.

16. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for recommending friend accounts according to any one of claims 1 to 10 is implemented.

17. A computer program product, characterized in that The computer program product includes a computer program, which is stored in a computer-readable storage medium; the computer program is read and executed from the computer-readable storage medium by a processor of a computer device, so that the computer device executes the friend account recommendation method as described in any one of claims 1 to 10.

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