Game clip recommendation method, device and storage medium

Abstract

The present invention relates to the field of game technology and discloses a game clip recommendation method, device and storage medium. The method comprises: when updating a game, obtaining user information and game update information, selecting a target game clip from preset game clips based on the user information and game update information, wherein the preset game clips are generated based on game information of multiple game players, and pushing the target game clips to the user; because the present invention provides the user with trial game clips based on the user information and game update information when the user updates the game, the interactivity during the game update can be improved, thereby improving the user experience.

Description

Technical Field

[0001] The present invention relates to the field of game technology, and in particular to a game clip recommendation method, device and storage medium. Background Art

[0002] Currently, game updates often require downloading large installation packages, which can take several minutes. In existing technologies, video ads are typically pushed to users during the waiting period, forcing them to passively accept them, resulting in a poor user experience. Summary of the Invention

[0003] The main purpose of the present invention is to provide a game clip recommendation method, device and storage medium, aiming to solve the existing technical problem that video advertisements are pushed to users during the waiting process, and users can only passively accept them, resulting in a poor user experience.

[0004] To achieve the above object, the present invention provides a game segment recommendation method, which includes the following steps:

[0005] When updating a game, obtain user information and game update information;

[0006] selecting a target game segment from preset game segments according to the user information and the game update information, wherein the preset game segments are generated based on game information of multiple game players;

[0007] Push the target game clip to the user.

[0008] Optionally, the step of selecting a target game segment from preset game segments according to the user information and the game update information includes:

[0009] Determining the user's favorite game mode and the user's favorite character type based on the user information;

[0010] Determining an update waiting time according to the user information and the game update information;

[0011] generating a user tag according to at least one of the user's favorite game mode, the user's favorite character type, and the update waiting time;

[0012] The user tag is matched with the game tag of the preset game clip, and a target game clip is selected from the preset game clips according to the matching result.

[0013] Optionally, the step of determining the user's favorite game mode and the user's favorite character type based on the user information includes:

[0014] determining, based on the user information, the number of times the user played each game mode and the number of times the user won each game mode;

[0015] Determining a user's favorite game mode based on the number of times the user played each game mode and the number of times the user won each game mode;

[0016] Determining, based on the user information, the number of times the user has played each game character and the number of times the user has won games using each game character;

[0017] The user's favorite character type is determined based on the number of times the user plays each game character and the number of times the user wins games using each game character.

[0018] Optionally, before the step of obtaining user information and game update information during game update, the process further includes:

[0019] Determine popular games based on game information of multiple game players;

[0020] Determining a game segment segmentation dimension corresponding to the popular game based on the game type of the popular game;

[0021] The popular games are segmented according to the game segment segmentation dimension to obtain preset game segments.

[0022] Optionally, after the step of segmenting the popular game according to the game segment segmentation dimension to obtain preset game segments, the step further includes:

[0023] Obtaining segment information of the preset game segment, wherein the segment information is at least one of a game mode, a character type, and a trial play segment duration;

[0024] A game tag corresponding to the preset game segment is constructed based on the segment information.

[0025] Optionally, after the step of pushing the target game clip to the user, the method further includes:

[0026] When the target game segment is a game segment of a target game, obtaining a trial play node and / or trial play duration when the user tries out the target game segment, and the target game is a game that is being updated;

[0027] When the trial play node and / or the trial play duration meets the preset synchronization conditions and the target game is updated, the user's trial play progress is synchronized to the target game.

[0028] Optionally, after the step of pushing the target game clip to the user, the method further includes:

[0029] When the user completes a preset task in the target game segment, searching for resource adjustment information corresponding to the preset task;

[0030] Resources are adjusted for the user according to the resource adjustment information.

[0031] Optionally, after the step of pushing the target game clip to the user, the method further includes:

[0032] When the user plays the target game segment, obtaining the historical trial play node and / or historical trial play duration when the user last tried to play the target game segment;

[0033] The play content of the target game segment is determined based on the historical trial play nodes and / or the historical trial play duration.

[0034] In addition, to achieve the above-mentioned purpose, the present invention also proposes a game fragment recommendation device, which includes a memory, a processor, and a game fragment recommendation program stored in the memory and executable on the processor, and the game fragment recommendation program is configured to implement the game fragment recommendation method described above.

[0035] In addition, to achieve the above-mentioned purpose, the present invention further proposes a storage medium, on which a game fragment recommendation program is stored. When the game fragment recommendation program is executed by a processor, the game fragment recommendation method as described above is implemented.

[0036] The present invention discloses that when a game is updated, user information and game update information are obtained, and a target game fragment is selected from preset game fragments based on the user information and game update information. The preset game fragment is generated based on the game information of multiple game players, and the target game fragment is pushed to the user. Since the present invention provides the user with a trial game fragment based on the user information and game update information when the user updates the game, the interactivity during the game update can be improved, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 1 is a schematic diagram of the structure of a game clip recommendation device in a hardware operating environment according to an embodiment of the present invention;

[0038] Figure 2 This is a flowchart of a first embodiment of a method for recommending game segments according to the present invention;

[0039] Figure 3 This is a schematic diagram of displaying trial play prompt information on a display interface of a terminal device according to an embodiment of a method for recommending game clips of the present invention;

[0040] Figure 4 This is a flowchart of a second embodiment of the game segment recommendation method of the present invention;

[0041] Figure 52 is a flowchart of a third embodiment of a method for recommending game segments according to the present invention.

[0042] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0043] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0044] Reference Figure 1 , Figure 1 This is a schematic diagram of a game fragment recommendation device structure for a hardware operating environment according to an embodiment of the present invention.

[0045] like Figure 1 As shown, the game clip recommendation device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. In the present invention, the wired interface of the user interface 1003 may be a USB interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a wireless fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (RAM) or a non-volatile memory (NVM), such as a disk storage device. The memory 1005 may also be a storage device independent of the processor 1001.

[0046] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the game segment recommendation device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0047] like Figure 1 As shown, the memory 1005 identified as a computer storage medium may include an operating system, a network communication module, a user interface module, and a game segment recommendation program.

[0048] exist Figure 1In the game fragment recommendation device shown, the network interface 1004 is mainly used to connect to the background server and communicate data with the background server; the user interface 1003 is mainly used to connect to the user device; the game fragment recommendation device calls the game fragment recommendation program stored in the memory 1005 through the processor 1001, and executes the game fragment recommendation method provided by the embodiment of the present invention.

[0049] Based on the above hardware structure, an embodiment of the game clip recommendation method of the present invention is proposed.

[0050] Reference Figure 2 , Figure 2 4 is a flowchart of the first embodiment of the game segment recommendation method of the present invention, which provides the first embodiment of the game segment recommendation method of the present invention.

[0051] In a first embodiment, the game clip recommendation method includes the following steps:

[0052] Step S10: When updating the game, obtain user information and game update information.

[0053] It should be understood that the execution subject of the method of this embodiment can be a game clip recommendation device with data processing, network communication and program running functions, such as a server, etc., or other electronic devices that can achieve the same or similar functions. This embodiment does not limit this.

[0054] It is understandable that when a game is being updated, the game being updated can be a regular game or a cloud game. In this embodiment and other embodiments, cloud games are used as an example for explanation.

[0055] It should be noted that user information may include user type, user behavior information, etc. User types may include new users, ordinary users, and first-time users. New users may be users who do not have corresponding game behavior information; first-time users may be users who trigger the download of a large resource pack when playing the game for the first time. User behavior information may include: 1. The player's unique identifier u; 2. The player's play history records the player's play time by game dimension. 3. For a specific game, count the number of times each character type is played {r1, r2, r3, r4, r5} and the corresponding number of wins {w1, w2, w3, w4, w5}. 4. Count the number of 1v1 games (pp1), 3v3 games (pp2), and 5v5 games (pp4) in Player Versus Player (PVP) mode, as well as the number of wins in Player Versus Player (PPW1), ppw2, and ppw4. PVP mode can be a player-versus-player game mode, typically an interactive competitive game mode where players use game resources to attack each other. 5. Count the number of Player Versus Environment (PVE) games (pe) and the number of PVE wins (pew). PVE mode can be a game mode where players challenge NPC monsters and bosses controlled by the game program.

[0056] The game update information may include information such as the game package that currently needs to be updated.

[0057] Step S20: selecting a target game segment from preset game segments according to the user information and the game update information, where the preset game segments are generated based on game information of multiple game players.

[0058] It should be understood that selecting the target game fragment from the preset game fragments based on the user information and the game update information may be when the user is a new user and there is no corresponding game behavior data, so the target game fragment can be randomly selected from the preset game fragments; when the user is an ordinary user, there is corresponding game behavior data, so the corresponding target game fragment can be matched from the preset game fragments based on the user information and game update information.

[0059] In a specific implementation, for example, when a user plays a game for the first time and triggers the download of a large resource package, the system randomly selects a target game clip from the top 10 popular cloud game video clips.

[0060] It is understood that the game information of multiple game players can be analyzed and preset game clips can be generated based on the analysis results. For example, the clips of the top 10 games ranked by cumulative play time of all players in a week can be used as preset game clips.

[0061] Step S30: Push the target game clip to the user.

[0062] It is understandable that pushing the target game clip to the user can be to send the trial game package corresponding to the target game clip to the terminal device corresponding to the user. After receiving the trial game package, the terminal device displays trial play prompt information on the display interface of the terminal device.

[0063] For easier understanding, refer to Figure 3 It is used to illustrate, but not to describe this solution. Figure 3 This is a schematic diagram of displaying trial play prompt information on the display interface of a terminal device. In the figure, a trial play prompt, an update progress bar, and a trial play button are displayed in the game update screen. Users can try out the target game segment by clicking the trial play button.

[0064] In the first embodiment, it is disclosed that when a game is updated, user information and game update information are obtained, and a target game fragment is selected from preset game fragments based on the user information and game update information. The preset game fragment is generated based on the game information of multiple game players, and the target game fragment is pushed to the user; because this embodiment provides the user with a trial game fragment based on the user information and game update information when the user updates the game, it can improve the interactivity of the game during the update, and thus improve the user experience.

[0065] Furthermore, in order to improve the user's trial play experience, after step S30, the following steps are further included:

[0066] When the target game segment is a game segment of a target game, obtaining a trial play node and / or trial play duration when the user tries out the target game segment, and the target game is a game that is being updated;

[0067] When the trial play node and / or the trial play duration meets the preset synchronization conditions and the target game is updated, the user's trial play progress is synchronized to the target game.

[0068] It should be understood that in order to improve the user's trial experience, in this embodiment, when the trial node and / or trial duration meets the preset synchronization conditions and the target game is updated, the user's trial progress will be synchronized to the target game.

[0069] It should be noted that the preset synchronization conditions can be set in advance. For example, the preset conditions can be completing game registration, trying the game for 3 minutes, etc.

[0070] In a specific implementation, for example, when the user's trial node is completed for game registration and the game being updated is updated, the user's trial progress is synchronized to the updated game. Otherwise, the trial progress is not synchronized.

[0071] Furthermore, in order to improve the user's enthusiasm for trial play, after step S30, the following steps are further included:

[0072] When the user completes a preset task in the target game segment, searching for resource adjustment information corresponding to the preset task;

[0073] Resources are adjusted for the user according to the resource adjustment information.

[0074] It is understandable that in order to improve the user's enthusiasm for trial play, in this embodiment, preset tasks can also be set, and when the user completes the task, resources are configured according to the resource adjustment information corresponding to the preset task.

[0075] It should be noted that the preset tasks can be pre-set, for example, the preset task can be completing the registration process. The resource adjustment information corresponding to the preset tasks can also be pre-set according to actual needs, for example, completing the registration process corresponds to increasing the game download speed, and / or completing the registration process corresponds to rewarding game resources.

[0076] In a specific implementation, for example, when a user completes the registration process in a target game segment, priority resource allocation such as game update download acceleration is given.

[0077] Furthermore, in order to ensure the user's trial play experience, after step S30, the following steps are further included:

[0078] When the user plays the target game segment, obtaining the historical trial play node and / or historical trial play duration when the user last tried to play the target game segment;

[0079] The play content of the target game segment is determined based on the historical trial play nodes and / or the historical trial play duration.

[0080] It should be understood that in order to ensure the user's trial play experience, in this embodiment, when the user plays the target game segment, the user can also be provided with the play content of the target game segment based on the historical trial play nodes and / or historical trial play duration when the user last tried the target game segment.

[0081] In a specific implementation, for example, when a user plays a target game segment, the target game segment is played starting from a historical play node when the user last played the target game segment.

[0082] Reference Figure 4 , Figure 4 This is a flow chart of the second embodiment of the game segment recommendation method of the present invention, based on the above Figure 2 The first embodiment shown provides a second embodiment of the game segment recommendation method of the present invention.

[0083] In the second embodiment, step S20 includes:

[0084] Step S201: Determine the user's favorite game mode and the user's favorite character type based on the user information.

[0085] It should be understood that, in order to improve the accuracy of selecting the target game segment, in this embodiment, the target game segment is selected based on the user tag and the trial play tag.

[0086] It is understandable that determining the user's favorite game mode and the user's favorite character type based on the user information may be performed by analyzing the user information to obtain the user's favorite game mode and the user's favorite character type.

[0087] Furthermore, in order to improve the accuracy of user information analysis, step S201 includes:

[0088] determining, based on the user information, the number of times the user played each game mode and the number of times the user won each game mode;

[0089] Determining a user's favorite game mode based on the number of times the user played each game mode and the number of times the user won each game mode;

[0090] Determining, based on the user information, the number of times the user has played each game character and the number of times the user has won games using each game character;

[0091] The user's favorite character type is determined based on the number of times the user plays each game character and the number of times the user wins games using each game character.

[0092] It should be understood that in order to improve the accuracy of user information analysis, in this embodiment, the user's favorite game mode is determined based on the number of times the user plays each game mode and the number of times the user wins each game mode, and the user's favorite character type is determined based on the number of times the user plays each game character and the number of times the user wins the game using each game character.

[0093] It can be understood that determining the user's favorite game mode based on the number of times the user plays each game mode and the number of times the user wins each game mode can be generating a game mode value for each game mode based on the number of times the user plays each game mode and the number of times the user wins each game mode, and determining the user's favorite game mode based on the game mode value.

[0094] In the specific implementation, the game mode value of 1V1 mode is taken as an example for explanation, but this solution is not limited. Where m u is the game mode value of 1V1 mode, pp1 is the number of times the user plays 1V1 mode, pp2 is the number of times the user plays 3V3 mode, pp4 is the number of times the user plays 5V5 mode, pe is the number of times the user plays PVE mode, and ppw1 is the number of times the user wins 1V1 mode. The game mode values ​​of other game modes can be obtained similarly.

[0095] It should be understood that determining the user's favorite game mode based on the game mode value may be to use the game mode with the largest game mode value as the user's favorite game mode.

[0096] It can be understood that determining the user's favorite character type based on the number of times the user plays each game character and the number of times the user wins the game using each game character can be determining the character type value of each character type based on the number of times the user plays each game character and the number of times the user wins the game using each game character, and determining the user's favorite character type based on the character type value.

[0097] In the specific implementation, the role type value of game character 1 is taken as an example for explanation, but this solution is not limited. Where r u is the character type value of game character 1, r1 is the number of times the user plays game character 1, r2 is the number of times the user plays game character 2, r3 is the number of times the user plays game character 3, r4 is the number of times the user plays game character 4, r5 is the number of times the user plays game character 5, w1 is the number of times the user wins the game using game character 1, and the character type values ​​of other game characters can be obtained similarly.

[0098] It should be understood that determining the user's favorite character type based on the character type value may be taking the character type with the largest character type value as the user's favorite character type.

[0099] Step S202: Determine the update waiting time according to the user information and the game update information.

[0100] It can be understood that determining the update waiting time based on user information and game update information can be determining the network currently used by the user based on the user information, determining the game package currently needing to be updated based on the game update information, and determining the update waiting time based on the network currently used by the user and the game package currently needing to be updated.

[0101] In the specific implementation, assuming that the network currently used by the user is 4G, 100Mbit / s, and the size of the game package currently to be updated is 2G, the user's update waiting time is calculated as

[0102] Step S203: Generate a user tag according to at least one of the user's favorite game mode, the user's favorite character type, and the update waiting time.

[0103] In the specific implementation, according to the user's favorite game mode m u 、Users’ favorite role types u And update waiting time to generate user tags {m u ,r u ,tu}.

[0104] Step S204: matching the user tag with the game tag of the preset game clip, and selecting a target game clip from the preset game clips according to the matching result.

[0105] It is understandable that matching the user tag with the game tag of the preset game clip and selecting the target game clip from the preset game clips based on the matching result can be calculating a set of associations between the user tag and the game tag of the preset game clip using a preset association formula, and selecting the target game clip from the preset game clips based on the association set, wherein the preset association formula is as follows:

[0106] R={i,f(i)},1≤i≤n

[0107]

[0108] In the formula, R is the correlation set, f(i) is the correlation between the user label and the trial label, and the user label is {m u ,r u ,t u}, the trial tag is {m i ,r i ,t i}, cloud game fragment set V = {v1, v2, ... v n}.

[0109] It should be understood that selecting the target game segment from the preset game segments according to the correlation set may be selecting the preset game segment corresponding to a correlation infinitely close to 1 from the correlation set as the target game segment.

[0110] In a second embodiment, it is disclosed that a user's favorite game mode and user's favorite character type are determined based on user information, an update waiting time is determined based on user information and game update information, a user tag is generated based on at least one of the user's favorite game mode, user's favorite character type, and update waiting time, the user tag is matched with a game tag of a preset game clip, and a target game clip is selected from the preset game clips based on the matching result; since this embodiment selects the target game clip based on the user tag and the trial play tag, the accuracy of the target game clip selection can be improved.

[0111] Reference Figure 5 , Figure 5 This is a flow chart of a third embodiment of a method for recommending game segments of the present invention. Based on the above embodiments, the third embodiment of the method for recommending game segments of the present invention is proposed.

[0112] In the third embodiment, before step S10, the method further includes:

[0113] Step S01: determining popular games based on game information of multiple game players.

[0114] It should be understood that, in order to ensure that the preset game segments meet the actual needs of users, in this embodiment, the popular games are segmented according to their game types to obtain the preset game segments.

[0115] It should be noted that game information may include information such as cumulative play time and the name of the game played.

[0116] It is understandable that determining popular games based on the game information of multiple game players can be to rank the top 10 games from high to low in terms of cumulative playing time of all players within a week as popular games.

[0117] Step S02: determining a game segment segmentation dimension corresponding to the popular game based on the game type of the popular game.

[0118] It should be noted that the dimensions of game segmentation may include game mode, player selectable character type, character skill level, game scene, and game difficulty, etc.

[0119] It is understood that determining the game segmentation dimensions corresponding to popular games based on their game types can be performed by searching a preset segmentation table for the game segmentation dimensions corresponding to the game types. The preset segmentation table includes a correspondence between game types and game segmentation dimensions, and the correspondence between game types and game segmentation dimensions can be pre-set. For example, the game segmentation dimensions corresponding to MOBA multiplayer competitive games are game mode, player selectable character type, and character skill level.

[0120] Step S03: Segment the popular game according to the game segment segmentation dimension to obtain preset game segments.

[0121] In the specific implementation, for the most popular MOBA multiplayer competitive game nowadays, the game scenes are first divided into α types according to the game mode, then the player's optional character types are counted as β types, and finally combined with the character's difficulty of getting started γ types, the number of preset trial clips of this popular game is obtained as αβλ.

[0122] Taking the game "XXXX" as an example, 1) the game mode is divided into PVP battle mode and PVE level-based mode. The PVP battle mode is divided into 1v1, 3v3, and 5v5 based on the number of players, resulting in a total of four game types. 2) In this game, players can choose from five character types: tank, warrior, shooter, assassin, mage, and support. 3) The player character's skill level is divided into three levels: high, medium, and low. This results in 4*5*3=60 trial play segments.

[0123] For survival competitive games, the sharding method is similar to that for multiplayer competitive games. Taking the game "YYYY" as an example, 1) the game mode is divided into PVE and PVP. 2) In PVP mode, depending on the player's choice of team mode, it is divided into 100-player competition, group confrontation (further subdivided into 2v2, 5v5, and 50v50 based on the number of team members), and the PVE mode, for a total of 4 modes. In each mode, players can choose from five character types: soldier, ninja, architect, scavenger, and survivor. These are further divided into three types based on the player's skill level: 3 minutes, 6 minutes, and 10 minutes, generating a total of 4*5*3=60 trial segments. For survival competitive games, a total of 60 trial segments are generated.

[0124] For player-created games, we first divide the game scenes into x types according to the game mode, then count the specific creation scenes in the game into y types, and finally combine the game's difficulty type z types to obtain the number of trial play segments of the game as xyz.

[0125] Taking the game "ZZZZ" as an example, 1) the game mode is divided into the following five types: survival mode, creative mode, adventure mode, and spectator mode. 2) Combined with the specific scenes in the game, a total of 13 types: day and night in the overworld, wastelands, soul sand valleys, crimson forests, warped forests, and basalt deltas in the Nether, and snowy worlds, mountain worlds, plains, hills, grasslands, and deserts in the End. 3) Combined with the game's difficulty types: peaceful, easy, normal, and hard, a total of 5*13*4=260 trial clips are generated.

[0126] In a third embodiment, it is disclosed that popular games are determined based on game information of multiple game players, game fragment segmentation dimensions corresponding to the popular games are determined based on the game types of the popular games, and the popular games are segmented according to the game fragment segmentation dimensions to obtain preset game fragments. Due to this embodiment, popular games are also segmented based on the game types of the popular games, thereby ensuring that the preset game fragments meet the actual needs of users.

[0127] In the third embodiment, after step S03, the method further includes:

[0128] Step S04: Obtaining the segment information of the preset game segment, wherein the segment information is at least one of a game mode, a character type, and a trial play segment duration.

[0129] It should be understood that in order to increase the speed of selecting the target game fragment, in this embodiment, trial play tags corresponding to the preset game fragments are pre-constructed so that the target game fragments can be selected based on the trial play tags later.

[0130] Step S05: Constructing a game tag corresponding to the preset game segment based on the segment information.

[0131] In the specific implementation, the most popular MOBA multiplayer competitive game is used for detailed explanation. Construct the trial tag corresponding to the preset game clip from the three dimensions of game mode, character type, and trial clip length: 1) Record the mode value range set {e, p1, ... p n}, where e represents the PVE mode, p represents the PVP mode, {p1,…p n} are the detailed classifications of the PVP model. 2) The set of character types {1, 2, ... n} that players can choose from. 3) The duration of the trial segment is p minutes.

[0132] Taking the game "XXXX" as an example, 1) the game mode is divided into PVP battle mode and PVE level-breaking mode. PVP battle mode is divided into 1v1, 3v3, and 5v5 modes based on the number of players on both sides. The mode value range is {1, 21, 23, 25}, where 1 represents the PVE mode and 2 represents the PVP mode. {21, 23, 25} are 1v1, 3v3, and 5v5 under the PVP model, respectively. 2) Players can choose from five character types: tank, warrior, shooter, assassin, mage, and support. The value range is defined as {1, 2, 3, 4, 5} based on the different character types. 3) Each segment is divided into three levels: 3 minutes, 6 minutes, and 10 minutes based on the player character's high, medium, and low skills. The value is defined by the actual segment length. 4) Based on the value range defined above, each trial segment is labeled with a corresponding value based on the three dimensions of game mode, character type, and trial segment length. For example, in a game clip video of a character A with a medium skill level playing a 5V5 battle for about 5.5 minutes, character A is a tank hero with a value of 1, and the corresponding trial play label is {25, 1, 5.5}.

[0133] In a third embodiment, obtaining fragment information of the preset game fragment is disclosed, where the fragment information is at least one of a game mode, a character type, and a trial fragment duration, and a game tag corresponding to the preset game fragment is constructed based on the fragment information; since this embodiment pre-constructs the trial fragment corresponding to the preset game fragment, it is convenient to subsequently select the target game fragment based on the trial fragment, thereby improving the selection speed of the target game fragment.

[0134] In addition, an embodiment of the present invention further provides a storage medium having a game fragment recommendation program stored thereon. When the game fragment recommendation program is executed by a processor, the game fragment recommendation method as described above is implemented.

[0135] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0136] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0137] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, or of course by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory image (ROM) / random access memory (RAM), a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0138] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for recommending game clips, characterized in that: The game clip recommendation method comprises the following steps: When updating a game, obtain user information and game update information; selecting a target game segment from preset game segments according to the user information and the game update information, wherein the preset game segments are generated based on game information of multiple game players; Pushing the target game clip to the user, wherein the target game clip is for the user to try out; The step of selecting a target game segment from preset game segments according to the user information and the game update information includes: Determining the user's favorite game mode and the user's favorite character type based on the user information; Determining an update waiting time according to the user information and the game update information; generating a user tag according to at least one of the user's favorite game mode, the user's favorite character type, and the update waiting time; The user tag is matched with the game tag of the preset game clip, and a target game clip is selected from the preset game clips according to the matching result.

2. The game clip recommendation method according to claim 1, wherein: The step of determining the user's favorite game mode and the user's favorite character type based on the user information includes: determining, based on the user information, the number of times the user played each game mode and the number of times the user won each game mode; Determining a user's favorite game mode based on the number of times the user played each game mode and the number of times the user won each game mode; Determining, based on the user information, the number of times the user has played each game character and the number of times the user has won games using each game character; The user's favorite character type is determined based on the number of times the user plays each game character and the number of times the user wins games using each game character.

3. The game clip recommendation method according to claim 1 or 2, wherein: Before the step of obtaining user information and game update information during game update, the method further includes: Determine popular games based on game information of multiple game players; Determining a game segment segmentation dimension corresponding to the popular game based on the game type of the popular game; The popular games are segmented according to the game segment segmentation dimension to obtain preset game segments.

4. The game clip recommendation method according to claim 3, wherein: After the step of segmenting the popular games according to the game segment segmentation dimension to obtain preset game segments, the method further includes: Obtaining segment information of the preset game segment, wherein the segment information is at least one of a game mode, a character type, and a trial play segment duration; A game tag corresponding to the preset game segment is constructed based on the segment information.

5. The game clip recommendation method according to claim 1 or 2, wherein: After the step of pushing the target game clip to the user, the method further includes: When the target game segment is a game segment of a target game, obtaining a trial play node and / or trial play duration when the user tries out the target game segment, and the target game is a game that is being updated; When the trial play node and / or the trial play duration meets the preset synchronization conditions and the target game is updated, the user's trial play progress is synchronized to the target game.

6. The game clip recommendation method according to claim 1 or 2, characterized in that: After the step of pushing the target game clip to the user, the method further includes: When the user completes a preset task in the target game segment, searching for resource adjustment information corresponding to the preset task; Resources are adjusted for the user according to the resource adjustment information.

7. The game clip recommendation method according to claim 1 or 2, characterized in that: After the step of pushing the target game clip to the user, the method further includes: When the user plays the target game segment, obtaining the historical trial play node and / or historical trial play duration when the user last tried to play the target game segment; The play content of the target game segment is determined based on the historical trial play nodes and / or the historical trial play duration.

8. A game clip recommendation device, characterized in that: The game fragment recommendation device includes: a memory, a processor, and a game fragment recommendation program stored in the memory and executable on the processor. When the game fragment recommendation program is executed by the processor, the game fragment recommendation method according to any one of claims 1 to 7 is implemented.

9. A storage medium, characterized in that: The storage medium stores a game segment recommendation program, which, when executed by a processor, implements the game segment recommendation method according to any one of claims 1 to 7.

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