Game teammate recommendation method and terminal

Abstract

The invention discloses a game teammate recommendation method and a terminal, according to game data of each player, an exclusive feature set corresponding to each player is created, and the exclusive feature set comprises a login time rule, role data, a current game target, a game style and a player IP; when a team forming request of a target player is received, whether the target player provides an intention feature set or not is judged, if yes, other online team forming players of which the exclusive feature sets conform to the intention feature set serve as recommended teammates, the recommended teammates are sent to the target player, and a team forming process is executed. The intention feature set is composed of at least one of a login time rule, role data, a current game target, a game style and a player IP. The current team forming requirement is met, the possibility that the target player makes a team forming request again at present is reduced, the probability that the target player and the recommended teammate become friends is improved, the satisfaction degree of the player to the teammate recommended by the server is improved, and the pressure of the server for processing team forming request data is reduced.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a method and terminal for recommending game teammates. Background Technology

[0002] Many online games nowadays have teammate matching features, recommending teammates to players when they form teams, making it easier for them to challenge dungeons and complete quests together. However, most current games only consider single elements such as player class or the level of the challenge when matching or recommending teammates.

[0003] In game modes like PUBG, which demand high levels of player cooperation, relying solely on a single element to team up or recommend teammates is unlikely to yield satisfactory results and can easily lead to a poor in-game experience. Furthermore, this method of recommending teammates can cause players to repeatedly click "refresh" buttons while searching for teammates, repeatedly sending data requests to the server. This can put pressure on server processing and cause response delays. To address this, some games implement restrictions, such as allowing players to click the "refresh" button again only after a preset time after multiple clicks. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and terminal for recommending game teammates, thereby improving players' satisfaction with teammates recommended by the server and reducing the pressure on the server to process team-up request data.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for recommending teammates in games includes the following steps: S1. Based on each player's game data, create a unique feature set for each player, including login time patterns, character data, current game goals, game style, and player IP. S2. Upon receiving a team-up request from a target player, determine whether the target player provides a set of intended characteristics. If so, recommend other online team-up players whose exclusive characteristic set matches the intended characteristic set as teammates and send the recommended teammates to the target player. The intended characteristic set consists of at least one of the following: login time pattern, character data, current game goal, game style, and player IP.

[0006] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows: A game teammate recommendation terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps: S1. Based on each player's game data, create a unique feature set for each player, including login time patterns, character data, current game goals, game style, and player IP. S2. Upon receiving a team-up request from a target player, determine whether the target player provides a set of intended characteristics. If so, recommend other online team-up players whose exclusive characteristic set matches the intended characteristic set as teammates and send the recommended teammates to the target player. The intended characteristic set consists of at least one of the following: login time pattern, character data, current game goal, game style, and player IP.

[0007] The beneficial effects of this invention are as follows: It provides a game teammate recommendation method and terminal, which establishes a unique feature set for each player, composed of multi-dimensional data such as login time patterns, character data, and current game goals. When a player requests to team up, the system recommends other online teammate players based on the player's provided feature set. This not only meets the needs of the current team in terms of game goals, game style, and game characters, reducing the possibility of the target player making another teammate request, but also optimizes the selection based on login time patterns and player IP, increasing the probability of the target player becoming friends with the recommended teammate. This helps the player reduce the need to team up with strangers in future games, improves player satisfaction with the teammate recommended by the server, and reduces the pressure on the server to process teammate request data. Attached Figure Description

[0008] Figure 1 This is a schematic diagram illustrating the steps of a game teammate recommendation method according to the present invention; Figure 2 This is a system block diagram of a game teammate recommendation terminal according to the present invention.

[0009] Label Explanation: 1. A game teammate recommendation terminal; 2. Memory; 3. Processor. Detailed Implementation

[0010] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0011] Please refer to Figure 1 A method for recommending game teammates includes the following steps: S1. Based on each player's game data, create a unique feature set for each player, including login time patterns, character data, current game goals, game style, and player IP. S2. Upon receiving a team-up request from a target player, determine whether the target player provides a set of intended characteristics. If so, recommend other online team-up players whose exclusive characteristic set matches the intended characteristic set as teammates and send the recommended teammates to the target player. The intended characteristic set consists of at least one of the following: login time pattern, character data, current game goal, game style, and player IP.

[0012] As can be seen from the above description, the beneficial effects of this invention are as follows: A unique feature set is established for each player, consisting of multi-dimensional data such as login time patterns, character data, and current game objectives. When a player requests to team up, other online teammates are recommended based on the player's provided feature set. This not only ensures that the current team meets the requirements of game objectives, game style, and game characters, reducing the possibility of the target player requesting to team up again, but also optimizes the selection based on login time patterns and player IP, increasing the probability of the target player becoming friends with recommended teammates. This helps reduce the need to team up with strangers in future games, increases player satisfaction with server-recommended teammates, and reduces the server's pressure in processing team-up request data.

[0013] Furthermore, step S2 also includes: If not, proceed to step S3; S3. Match the target player's unique feature set with the unique feature sets of other online team players, and provide recommended teammates to the target player based on the matching results.

[0014] As can be seen from the above description, if the target player does not provide a set of intended characteristics, matching can be performed based on the target player's own unique set of characteristics, so as to ensure that the target player has like-minded and well-coordinated teammates and optimize the player's in-game experience.

[0015] Furthermore, the procedure before step S2 includes: Set a comprehensive matching score, and assign a weight ratio to each item in the exclusive feature set based on the full score of the comprehensive matching score; Step S3 specifically involves: S31. Compare the similarity of one item of the target player's exclusive feature set with the corresponding item of the exclusive feature set of other online team players. If the comparison result falls within the corresponding preset comparison range, it means that the compared item has obtained the corresponding weight ratio score. Compare each item of the exclusive feature set in a loop until the overall matching score of each player after comparison with the target player is obtained. S32. Players whose overall match score is greater than or equal to a preset score are recommended teammates and provided to the target player.

[0016] As described above, when matching the target player's unique feature set with the unique feature sets of other online team players, the similarity of each item with the same content is compared one by one, and a score is given in a weighted proportion. The final total score is used to indicate whether other online team players are suitable as the target player's intended teammates, making the final recommendation results more accurate and reasonable.

[0017] Furthermore, after sending the recommended teammates to the target player, the process also includes: Provide the target player with an expand button to display detailed information for each recommended teammate; Upon receiving the action of the target player clicking the "Expand Details" button, each item of the exclusive feature set of the recommended teammate corresponding to the "Expand Details" button is displayed.

[0018] As can be seen from the above description, the feature of providing detailed information display makes it easier for players to fully understand their teammates' situations and find the most suitable teammates.

[0019] Furthermore, the procedure before step S2 includes: Online players are provided with a button to switch their team intentions, which includes options to accept or reject a team invitation. Online players who accept the team invitation are considered to be online team players.

[0020] As can be seen from the above description, when recommending teammates, in addition to players who are currently forming a team, some players who are not forming a team but are willing to accept team invitations are also used for information comparison, thereby expanding the matching range and improving recommendation efficiency and satisfaction.

[0021] Please refer to Figure 2 A game teammate recommendation terminal 1 includes a memory 2, a processor 3, and a computer program stored on the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, it performs the following steps: S1. Based on each player's game data, create a unique feature set for each player, including login time patterns, character data, current game goals, game style, and player IP. S2. Upon receiving a team-up request from a target player, determine whether the target player provides a set of intended characteristics. If so, recommend other online team-up players whose exclusive characteristic set matches the intended characteristic set as teammates and send the recommended teammates to the target player. The intended characteristic set consists of at least one of the following: login time pattern, character data, current game goal, game style, and player IP.

[0022] As can be seen from the above description, the beneficial effects of this invention are as follows: A unique feature set is established for each player, consisting of multi-dimensional data such as login time patterns, character data, and current game objectives. When a player requests to team up, other online teammates are recommended based on the player's provided feature set. This not only ensures that the current team meets the requirements of game objectives, game style, and game characters, reducing the possibility of the target player requesting to team up again, but also optimizes the selection based on login time patterns and player IP, increasing the probability of the target player becoming friends with recommended teammates. This helps reduce the need to team up with strangers in future games, increases player satisfaction with server-recommended teammates, and reduces the server's pressure in processing team-up request data.

[0023] Furthermore, step S2 also includes: If not, proceed to step S3; S3. Match the target player's unique feature set with the unique feature sets of other online team players, and provide recommended teammates to the target player based on the matching results.

[0024] As can be seen from the above description, if the target player does not provide a set of intended characteristics, matching can be performed based on the target player's own unique set of characteristics, so as to ensure that the target player has like-minded and well-coordinated teammates and optimize the player's in-game experience.

[0025] Furthermore, the procedure before step S2 includes: Set a comprehensive matching score, and assign a weight ratio to each item in the exclusive feature set based on the full score of the comprehensive matching score; Step S3 specifically involves: S31. Compare the similarity of one item of the target player's exclusive feature set with the corresponding item of the exclusive feature set of other online team players. If the comparison result falls within the corresponding preset comparison range, it means that the compared item has obtained the corresponding weight ratio score. Compare each item of the exclusive feature set in a loop until the overall matching score of each player after comparison with the target player is obtained. S32. Players whose overall match score is greater than or equal to a preset score are recommended teammates and provided to the target player.

[0026] As described above, when matching the target player's unique feature set with the unique feature sets of other online team players, the similarity of each item with the same content is compared one by one, and a score is given in a weighted proportion. The final total score is used to indicate whether other online team players are suitable as the target player's intended teammates, making the final recommendation results more accurate and reasonable.

[0027] Furthermore, after sending the recommended teammates to the target player, the process also includes: Provide the target player with an expand button to display detailed information for each recommended teammate; Upon receiving the action of the target player clicking the "Expand Details" button, each item of the exclusive feature set of the recommended teammate corresponding to the "Expand Details" button is displayed.

[0028] As can be seen from the above description, the feature of providing detailed information display makes it easier for players to fully understand their teammates' situations and find the most suitable teammates.

[0029] Furthermore, the procedure before step S2 includes: Online players are provided with a button to switch their team intentions, which includes options to accept or reject a team invitation. Online players who accept the team invitation are considered to be online team players.

[0030] As can be seen from the above description, when recommending teammates, in addition to players who are currently forming a team, some players who are not forming a team but are willing to accept team invitations are also used for information comparison, thereby expanding the matching range and improving recommendation efficiency and satisfaction.

[0031] Please refer to Figure 1 Embodiment 1 of the present invention is as follows: A method for recommending teammates in games includes the following steps: S1. Based on each player's game data, create a unique feature set for each player. The unique feature set includes login time patterns, character data, current game goals, game style, and player IP. In this embodiment, the exclusive feature set may also include, but is not limited to: online game time, historical teammate evaluations, out-of-game interests, and social interaction tendencies. The components are explained as follows: Login time patterns: This can be derived from analyzing player login time data. For example, it can be found that players tend to log in on weekday evenings or weekend days.

[0032] Game online time: By combining the online time data after each player login, the average weekly online time of players is determined, and the fluctuation of their online time is analyzed.

[0033] Game style: Distinguish players' specific behaviors in the game, such as fighting monsters, challenging dungeons, and completing tasks, to determine the player's behavior pattern. For example, some players are keen on dungeon challenges, while others are keen on building. Or, players' game styles can be described as aggressive, steady, casual, etc.

[0034] Character data: Character data has different interpretations depending on the type of game. Common character data includes character name, class, level, attributes, clothing, game behavior, game skill specialties, etc.; evaluate the effects of different class combinations, analyze the advantages and complementarity of different class combinations such as warrior, mage, priest in the game, and classify various skill specialties such as melee output, ranged attack, healing support, etc.

[0035] Social interaction tendency: Distinguishing between active and passive social tendencies can be determined by a player's behavior in the game, indicating whether they prefer to initiate communication or respond passively.

[0036] Current game objectives: Clearly define the player's current game objectives. Players can select or fill in their own objectives, such as leveling up, collecting equipment, or improving their ranking.

[0037] Historical team-up experience and evaluation reference: Analyze past team-up data to see how other players cooperated and how they were evaluated when they teamed up with other players in the past; based on this, select groups of players who cooperate well.

[0038] Player IPs take into account regional and cultural differences, such as gaming habits and cultural characteristics in different regions, in order to select players with similar backgrounds.

[0039] Interests outside the game: This section summarizes the categories of interests outside the game, filled in by the players, such as movie, music, sports, etc., and is used to recommend players with similar interests.

[0040] S2. Upon receiving a team-up request from a target player, determine whether the target player provides a set of intended characteristics. If so, recommend other online team-up players whose exclusive characteristic set matches the set of intended characteristics as teammates and send the recommended teammates to the target player. If not, proceed to step S3. The set of intended characteristics consists of at least one of the following: login time pattern, character data, current game goal, game style, and player IP. In this embodiment, the target player can create a set of intended features based on the current matching requirements. For example, if the game character is required to be a Paladin and the game style is to be enthusiastic about dungeon challenges, it means that the target player wants to find a Paladin to run dungeons with.

[0041] In this embodiment, a team-up intention switching button is provided to online players. The team-up intention switching button includes accepting the team-up invitation and rejecting the team-up invitation. Online players who accept the team-up invitation are regarded as players who form an online team.

[0042] S3. Match the target player's unique set of characteristics with the unique set of characteristics of other online team players, and provide recommended teammates to the target player based on the matching results.

[0043] In this embodiment, the method further includes the following steps before step S2: Set a comprehensive matching score and assign a weight ratio based on the maximum score for each item in the exclusive feature set; Regarding the weighting ratio, character data can be divided into character class combinations, game skill specialties, and overlapping game behaviors. An example of the preferred allocation is as follows: Login time pattern: Considering the importance of the synchronization of players' online time for team cooperation, it can be given a high weight, such as 30%.

[0044] Online game time: Reflects the player's level of engagement in the game, with a weighting of up to 20%.

[0045] Overlapping game behaviors: Similar game behaviors help improve team collaboration efficiency, with a weight of 20%.

[0046] Game style preference: Style compatibility can reduce internal team conflicts, with a weight of 15%.

[0047] Role-class pairing: A reasonable pairing of roles can improve team strength, with a weight of 10%.

[0048] Social interaction tendency: A good interaction tendency is conducive to good communication, with a weight of 5%.

[0049] Game Skill Specialties: Complementary skills can enhance the team's overall capabilities, with a weight of 5%.

[0050] Current game objective: A shared objective gives the team more direction, with a weight of 5%.

[0051] Past team performance evaluation: A good track record of cooperation is an important reference, with a weight of 5%.

[0052] Regional and cultural background (player IP): Similar backgrounds may facilitate better communication, with a weight of 3%.

[0053] Interests outside the game: Shared interests can increase resonance between players, with a weight of 2%.

[0054] Step S3 is as follows: S31. Compare the similarity of one item in the exclusive feature set of the target player with the corresponding item in the exclusive feature set of other online team players. If the comparison result falls within the corresponding preset comparison range, it means that the compared item has obtained the corresponding weight ratio score. Compare each item in the exclusive feature set in a loop until the overall matching score of each player after comparison with the target player is obtained. In this embodiment, similarity comparison refers to the fact that, in the same project, the target player and other online players should be the same or similar. For example, login time patterns: the target player hopes that teammates' login time includes 13:00 on weekends. The preset comparison range is that recommended teammates can be those who usually log in between 12:30 and 13:30 on weekends. Another example is overlapping game behavior: for example, when the target player fights monsters in the game, they mostly prioritize clearing monsters from the outer edge of the map and then explore the central area. The preset comparison range can be that other players also mostly prioritize clearing monsters from the outer edge of the map and then explore the central area, that is, the two have the same game habits.

[0055] As for calculating the overall matching score, in addition to directly using the full score (e.g., 100 points) multiplied by the aforementioned weighting ratio, each item can also be scored separately, and then the final score can be obtained according to its weighting ratio, as follows: Suppose another player's data scores are as follows when compared to the target player: - Login time regularity score: 80 points (out of 100) -Game online time rating: 70 points -Game behavior overlap score: 90 points -Game style preference rating: 85 points -Character class synergy rating: 90 points -Social interaction tendency score: 80 points -Game skill / talent rating: 85 points Current game target score: 95 points -Past team evaluation rating: 90 points -Regional and cultural background (player IP) rating: 75 points -Out-of-game hobbies rating: 80 points The player's overall match score relative to the target player is calculated as follows: 80*0.3+70*0.2+90*0.2+85*0.15+90*0.1+80*0.05+85*0.05+95*0.05+90*0.05+75*0.03+80*0.02=99.1 points S32. Recommend players whose overall match score is greater than or equal to a preset score as teammates and provide them to the target player. The preset score can be set by the target player or a relatively reasonable standard score can be given by the system.

[0056] In this embodiment, after sending the recommended teammates to the target player, the method further includes: providing the target player with an expand button for the detailed information of each recommended teammate; and after receiving the target player's click on the expand button, displaying each item of the unique feature set of the recommended teammate corresponding to the expand button, as shown in the following example: On one hand, there's the recommendation list format: Players are shown a list of recommended teammates, with each player's information including their game nickname, class, level, main game behavior preferences, and game goals. For example: Player nickname: [B] -Class: [Mage] - Level:

[50] -Main gaming behavior preference: [Enjoys challenging high-difficulty dungeons] -Game objective: [Obtain top-tier equipment] On the other hand, detailed information is displayed: players can click on a recommended teammate's entry in the list to view more detailed information, including past team evaluations, region, and off-game interests. For example: -Past team performance evaluation: [When teaming up with Player A, we cooperated well and were able to effectively complete dungeon challenges.] -Region: [From a certain province] -Interests outside of gaming: [Enjoy movies and music] On the other hand, visualization charts: Charts can be used to visually display the compatibility between recommended teammates and players. For example, bar charts can be used to represent the degree of compatibility in various dimensions, with darker colors indicating higher compatibility.

[0057] Please refer to Figure 2 Embodiment two of the present invention is as follows: A game teammate recommendation terminal 1 includes a memory 2, a processor 3, and a computer program stored on the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, it implements a game teammate recommendation method as described in Embodiment 1.

[0058] In summary, this invention provides a game teammate recommendation method and terminal. It establishes a unique feature set for each player, composed of multi-dimensional data such as login time patterns, character data, and current game objectives. When a player requests a team, the method recommends other online teammates based on the player's provided feature set. This not only ensures that the current team meets the player's needs in terms of game objectives, playstyle, and character roles, reducing the likelihood of the target player requesting a team again, but also optimizes the selection based on login time patterns and player IP addresses, increasing the probability of the target player becoming friends with recommended teammates. This helps reduce the need to team up with strangers in future games, improves player satisfaction with server-recommended teammates, and reduces the server's pressure in processing team request data.

[0059] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for recommending game teammates, characterized in that, Includes the following steps: S1. Based on each player's game data, create a unique feature set for each player, including login time patterns, character data, current game goals, game style, and player IP. S2. Upon receiving a team-up request from a target player, determine whether the target player provides a set of intended characteristics. If so, recommend other online team-up players whose exclusive characteristic set matches the intended characteristic set as teammates and send the recommended teammates to the target player. The intended characteristic set consists of at least one of the following: login time pattern, character data, current game goal, game style, and player IP.

2. The game teammate recommendation method according to claim 1, characterized in that, Step S2 further includes: If not, proceed to step S3; S3. Match the target player's unique feature set with the unique feature sets of other online team players, and provide recommended teammates to the target player based on the matching results.

3. The game teammate recommendation method according to claim 2, characterized in that, The procedure preceding step S2 also includes: Set a comprehensive matching score, and assign a weight ratio to each item in the exclusive feature set based on the full score of the comprehensive matching score; Step S3 specifically involves: S31. Compare the similarity of one item of the target player's exclusive feature set with the corresponding item of the exclusive feature set of other online team players. If the comparison result falls within the corresponding preset comparison range, it means that the compared item has obtained the corresponding weight ratio score. Compare each item of the exclusive feature set in a loop until the overall matching score of each player after comparison with the target player is obtained. S32. Players whose overall match score is greater than or equal to a preset score are recommended teammates and provided to the target player.

4. The game teammate recommendation method according to claim 1, characterized in that, After sending the recommended teammates to the target player, the process also includes: Provide the target player with an expand button to display detailed information for each recommended teammate; Upon receiving the action of the target player clicking the "Expand Details" button, each item of the exclusive feature set of the recommended teammate corresponding to the "Expand Details" button is displayed.

5. The game teammate recommendation method according to claim 1, characterized in that, The procedure preceding step S2 also includes: Online players are provided with a button to switch their team intentions, which includes options to accept or reject a team invitation. Online players who accept the team invitation are considered to be online team players.

6. A game teammate recommendation terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it performs the following steps: S1. Based on each player's game data, create a unique feature set for each player, including login time patterns, character data, current game goals, game style, and player IP. S2. Upon receiving a team-up request from a target player, determine whether the target player provides a set of intended characteristics. If so, recommend other online team-up players whose exclusive characteristic set matches the intended characteristic set as teammates and send the recommended teammates to the target player. The intended characteristic set consists of at least one of the following: login time pattern, character data, current game goal, game style, and player IP.

7. A game teammate recommendation terminal according to claim 6, characterized in that, Step S2 further includes: If not, proceed to step S3; S3. Match the target player's unique feature set with the unique feature sets of other online team players, and provide recommended teammates to the target player based on the matching results.

8. A game teammate recommendation terminal according to claim 7, characterized in that, The procedure preceding step S2 also includes: Set a comprehensive matching score, and assign a weight ratio to each item in the exclusive feature set based on the full score of the comprehensive matching score; Step S3 specifically involves: S31. Compare the similarity of one item of the target player's exclusive feature set with the corresponding item of the exclusive feature set of other online team players. If the comparison result falls within the corresponding preset comparison range, it means that the compared item has obtained the corresponding weight ratio score. Compare each item of the exclusive feature set in a loop until the overall matching score of each player after comparison with the target player is obtained. S32. Players whose overall match score is greater than or equal to a preset score are recommended teammates and provided to the target player.

9. A game teammate recommendation terminal according to claim 6, characterized in that, After sending the recommended teammates to the target player, the process also includes: Provide the target player with an expand button to display detailed information for each recommended teammate; Upon receiving the action of the target player clicking the "Expand Details" button, each item of the exclusive feature set of the recommended teammate corresponding to the "Expand Details" button is displayed.

10. A game teammate recommendation terminal according to claim 6, characterized in that, The procedure preceding step S2 also includes: Online players are provided with a button to switch their team intentions, which includes options to accept or reject a team invitation. Online players who accept the team invitation are considered to be online team players.

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