CN-121971863-A - Information recommendation method and device, electronic equipment and readable storage medium

CN121971863ACN 121971863 ACN121971863 ACN 121971863ACN-121971863-A

Abstract

The application discloses an information recommendation method, an information recommendation device, electronic equipment and a readable storage medium, which belong to the technical field of information recommendation; the game interaction method comprises the steps of predicting game interaction compactness between two users based on user data of any two users, predicting target game interaction behaviors matched with the two users based on the game interaction compactness, generating interaction guiding information based on the target game interaction behaviors and recommending the interaction guiding information to the two users to guide the two users to execute the target game interaction behaviors, and therefore adhesiveness between players can be improved, and accordingly the retention rate of the players is improved.

Inventors

LI XUAN

Assignees

网易（杭州）网络有限公司

Dates

Publication Date: 20260505
Application Date: 20251223

Claims (15)

1. An information recommendation method, comprising: Acquiring user data of each user in the game; predicting game interaction compactness between any two users based on user data of the two users; predicting target game interaction behaviors matched with the two users based on the game interaction compactness; And generating interaction guiding information based on the target game interaction behavior and pushing the interaction guiding information to the two users so as to guide the two users to execute the target game interaction behavior.
2. The method of claim 1, wherein predicting a target game interaction behavior that matches the two users based on the game interaction compactness comprises: Based on the game interaction compactness, predicting the matching degree of the two users and various game interaction behaviors, wherein the matching degree is used for indicating the probability of the two users executing the game interaction behaviors; and determining target game interaction behaviors matched with the two users from various game interaction behaviors based on the matching degree.
3. The method of claim 1, wherein the user data includes personal attribute data, historical game behavior data, and game social relationship data, wherein predicting game interaction affinity between any two users based on the user data of the two users comprises: calculating attribute similarity between any two users based on the personal attribute data of the two users; Calculating a game social style complementarity between the two users based on the game social style types of the two users, the game social style types determined based on the historical game behavior data; Calculating a game social affinity between the two users based on the game social relationship data of the two users; and carrying out fusion processing on the attribute similarity, the game social style complementation degree and the game social compactness to obtain the game interaction compactness between the two users.
4. A method according to claim 3, wherein the game social style type is determined by: counting the historical game behavior data of the user to obtain behavior counting characteristics; And determining the game social style type of the user from the candidate game social style types based on the behavior statistical characteristics.
5. The method of claim 4, wherein the candidate game social style type has a corresponding type condition, wherein the determining the user's game social style type from the candidate game social style types based on the behavioral statistics comprises: determining a type condition which is met by the behavior statistical characteristic in the type conditions; and determining the candidate game social style type corresponding to the type condition satisfied by the behavior statistical characteristic as the game social style type of the user.
6. The method of claim 3, wherein the calculating a game social affinity between the two users based on the game social relationship data of the two users comprises: Determining a first affinity based on a number of users in a game social network structure that have social relationships with the two users at the same time, the game social network structure determined based on the game social relationship data and the game social network structure including other users that have social relationships with the two users; Determining a second affinity based on the group to which the two users belong in the game social network structure; determining a third compactness based on interaction information corresponding to the two users in the game social network structure; and carrying out fusion processing on the first compactness, the second compactness and the third compactness to obtain the game social compactness between the two users.
7. The method of claim 1, wherein the obtaining user data for each user in the game comprises: Acquiring real-time game behavior data of the user; And under the condition that the real-time game behavior data meet preset recommendation conditions, acquiring user data of each user in the game.
8. The method of claim 1, wherein the target game interaction behavior comprises one of intra-plague collaboration behavior, player-to-player play behavior, casual play behavior, team-to-team copy behavior, virtual item gifting, private chat initiation, and friend addition.
9. The method according to any one of claims 1-8, further comprising: acquiring a task preference type corresponding to the user, wherein the task preference type of the user is determined based on the user data; determining probability values of various game tasks based on task preference types corresponding to the two users; and determining a target game task from the game tasks based on the probability value, and pushing the target game task to the two users.
10. The method of claim 9, wherein the task types of the game tasks include a combat type and a leisure type, wherein the determining the probability values of the various game tasks based on the task preference types corresponding to the two users comprises: determining that the probability value of the game task of the combat type is a first probability value and the probability value of the game task of the leisure type is a second probability value under the condition that the task preference types corresponding to the two users are combat preference types; And/or determining that the probability value of the game task of the combat type is a second probability value and the probability value of the game task of the leisure type is a first probability value under the condition that the task preference types corresponding to the two users are leisure preference types; And/or, determining that the probability value of the game task of the combat type and the probability value of the game task of the leisure type are both third probability values when the task preference types corresponding to the two users both comprise the combat preference type and the leisure preference type.
11. The method of claim 9, wherein the task preference type is determined based on: Acquiring fight force indication information of the two users and/or speaking times of the two users in a designated time period, wherein the fight force indication information and the speaking times are determined based on the user data; under the condition that the fight force indication information of the two users meets a first fight condition, determining the task preference type corresponding to the two users as a fight preference type; And/or determining the task preference type corresponding to the two users as a leisure preference type under the condition that the speaking times of the two users in the appointed duration are larger than the appointed times; And/or determining the task preference type corresponding to the two users as a combat preference type and a leisure preference type under the condition that the combat force indication information of the two users meets a first combat condition and the speaking times of the two users in the appointed duration are larger than the appointed times.
12. The method of claim 9, further displaying a trigger control for the target game task and a chat control when displaying the target game task, the trigger control for launching the target game task, the chat control for triggering the display of a chat interface including a link for the target game task thereon, the link for the target game task for being sent to the other of the two users.
13. An information recommendation device, characterized by comprising: the data acquisition module is used for acquiring user data of each user in the game; the first prediction module is used for predicting game interaction compactness between any two users based on user data of the two users; the second prediction module is used for predicting target game interaction behaviors matched with the two users based on the game interaction compactness; and the information recommendation module is used for generating interaction guide information based on the target game interaction behavior and recommending the interaction guide information to the two users so as to guide the two users to execute the target game interaction behavior.
14. An electronic device comprising a processor and a memory, the memory storing a plurality of instructions, the processor loading instructions from the memory to perform the steps of the information recommendation method according to any one of claims 1 to 12.
15. A computer-readable storage medium, having stored thereon a computer program, the computer program being loaded by a processor to perform the steps of the information recommendation method of any of claims 1 to 12.

Description

Information recommendation method and device, electronic equipment and readable storage medium Technical Field The present application relates to the field of information recommendation technologies, and in particular, to an information recommendation method, an apparatus, an electronic device, and a readable storage medium. Background With the development of the internet, electronic games have become one of the important entertainment modes for human beings, and the variety of electronic games is increasingly abundant, such as massive multiplayer online role playing games (MASSIVELY MULTIPLAYER ONLINE ROLE-PLAYING GAME, MMORPG) and Action games (ACT). In massively multiplayer online role-playing games, the stickiness between players can increase the player's retention. Currently, in order to increase the adhesiveness between players, friends are recommended to players by using team data or common friends. However, recommending only friends is more singular, resulting in a lower retention for the player. Disclosure of Invention The application provides an information recommendation method, an information recommendation device, electronic equipment and a storage medium, which can improve the adhesiveness between players, so that the retention rate of the players is improved. In a first aspect, an embodiment of the present application provides an information recommendation method, including: Acquiring user data of each user in the game; Predicting game interaction compactness between any two users based on user data of the two users; predicting target game interaction behaviors matched with the two users based on the game interaction compactness; Generating interaction guiding information based on the target game interaction behavior and recommending the interaction guiding information to the two users so as to guide the two users to execute the target game interaction behavior. In a second aspect, an embodiment of the present application provides an information recommendation apparatus, including: the data acquisition module is used for acquiring user data of each user in the game; the first prediction module is used for predicting game interaction compactness between any two users based on the user data of the two users; The second prediction module is used for predicting target game interaction behaviors matched with the two users based on the game interaction compactness; And the information recommending module is used for generating interactive guiding information based on the target game interactive behaviors and recommending the interactive guiding information to the two users so as to guide the two users to execute the target game interactive behaviors. In a third aspect, an embodiment of the present application further provides an electronic device, including a memory storing a plurality of instructions, where the processor loads the instructions from the memory to execute any one of the information recommendation methods provided in the embodiments of the present application. In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, where a plurality of instructions are stored, where the instructions are adapted to be loaded by a processor to execute any one of the information recommendation methods provided in the embodiments of the present application. According to the method and the device for achieving the game interaction, user data of all users in a game are obtained, game interaction compactness between two users is predicted based on the user data of any two users, target game interaction behaviors matched with the two users are predicted based on the game interaction compactness, interaction guiding information is generated based on the target game interaction behaviors and is recommended to the two users to guide the two users to execute the target game interaction behaviors, various game interaction behaviors are recommended to players, recommendation content is enriched instead of only recommending account numbers with friends, social ice breaking of the players is effectively facilitated, viscosity between the players is improved, and accordingly the retention rate of the players is improved. Drawings In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Fig. 1 is a schematic view of a scenario of an information recommendation method according to some embodiments of the present application; FIG. 2 is a flowchart illustrating an information recommendation method according to some embodiments of the present application; FIG. 3 is a schematic di