CN110917628A - Method, apparatus, and computer storage medium for determining user grouping - Google Patents
Method, apparatus, and computer storage medium for determining user grouping Download PDFInfo
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- CN110917628A CN110917628A CN201811102642.7A CN201811102642A CN110917628A CN 110917628 A CN110917628 A CN 110917628A CN 201811102642 A CN201811102642 A CN 201811102642A CN 110917628 A CN110917628 A CN 110917628A
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/70—Game security or game management aspects
- A63F13/79—Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories
- A63F13/795—Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories for finding other players; for building a team; for providing a buddy list
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
- A63F2300/50—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by details of game servers
- A63F2300/55—Details of game data or player data management
- A63F2300/5546—Details of game data or player data management using player registration data, e.g. identification, account, preferences, game history
- A63F2300/5566—Details of game data or player data management using player registration data, e.g. identification, account, preferences, game history by matching opponents or finding partners to build a team, e.g. by skill level, geographical area, background, play style
Abstract
Embodiments of the present disclosure relate to methods, apparatuses, and computer storage media for determining user groupings. In one embodiment, a method for determining user groupings is presented. The method comprises the following steps: acquiring behavior attribute data of a first user and behavior attribute data of a second user; comparing a plurality of behavior attributes in the behavior attribute data of the first user with a corresponding plurality of behavior attributes in the behavior attribute data of the second user respectively to obtain a corresponding plurality of difference scores; determining a total difference score for the first user and the second user based on the plurality of difference scores; and responsive to the total difference score being less than or equal to a predetermined value, dividing the first user and the second user into the same group. In one embodiment, an apparatus and computer storage medium for determining user groupings is presented.
Description
Technical Field
Embodiments of the present disclosure relate to user management, and more particularly, to a method, apparatus, and computer storage medium for grouping users logged in to an application server.
Background
With the development of internet technology, users engaged in activities such as games on the internet have increased significantly, resulting in a huge amount of game attribute data. Since these game attribute data can show the game behavior of the users in the game activities, these data are usually extracted and analyzed, so that users with similar game attribute data can match together as much as possible to play the game as teammates or opponents, thereby increasing the entertainment of the game. However, in conventional matching operations, the operations on the attribute data are generally not extensible. Therefore, it is desirable to enable dynamic, extensible attribute data manipulation for matching operations.
Disclosure of Invention
Embodiments of the present disclosure provide a scheme for determining user groupings.
According to a first aspect of the present disclosure, a method for determining a user group is presented. The method comprises the following steps: acquiring behavior attribute data of a first user and behavior attribute data of a second user; comparing a plurality of behavior attributes in the behavior attribute data of the first user with a corresponding plurality of behavior attributes in the behavior attribute data of the second user respectively to obtain a corresponding plurality of difference scores; determining a total difference score for the first user and the second user based on the plurality of difference scores; and responsive to the total difference score being less than or equal to a predetermined value, dividing the first user and the second user into the same group.
According to a second aspect of the present disclosure, a device for determining a user group is presented. The apparatus comprises: at least one processing unit; at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions when executed by the at least one processing unit, cause the apparatus to perform acts comprising: acquiring behavior attribute data of a first user and behavior attribute data of a second user; comparing a plurality of behavior attributes in the behavior attribute data of the first user with a corresponding plurality of behavior attributes in the behavior attribute data of the second user respectively to obtain a corresponding plurality of difference scores; determining a total difference score for the first user and the second user based on the plurality of difference scores; and responsive to the total difference score being less than or equal to a predetermined value, dividing the first user and the second user into the same group.
In a third aspect of the disclosure, a computer storage medium is provided. The computer storage medium has computer-readable program instructions stored thereon for performing the method according to the first aspect.
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the disclosure, nor is it intended to be used to limit the scope of the disclosure.
Drawings
The foregoing and other objects, features and advantages of the disclosure will be apparent from the following more particular descriptions of exemplary embodiments of the disclosure as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the disclosure.
FIG. 1 illustrates a schematic diagram of an example environment in which embodiments of the present disclosure can be implemented;
FIG. 2 illustrates a flow chart of a method of determining user groupings according to an embodiment of the present disclosure;
FIG. 3 illustrates a flow chart of a method of obtaining behavior attribute data prior to determining user groupings according to an embodiment of the present disclosure;
FIG. 4 illustrates a flow chart of a method of obtaining behavior attribute data prior to determining user groupings according to an embodiment of the present disclosure;
FIG. 5 illustrates a flow chart of a method of determining user groupings in accordance with an embodiment of the present disclosure; and
FIG. 6 illustrates a schematic block diagram of an example device that can be used to implement embodiments of the present disclosure.
Detailed Description
Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.
The demands of users for playing games are various. For example, for a Player to play against a Player (PVP) game, the user may be more focused on the experience of the game play. With the advent of more game types with social goals, such as "you guess me", "langer kill", etc., the user's historical performance during the game does not accurately reflect aspects of the user's behavior. Thus, merely basing the game attribute data on the user is not sufficient to match the user to a suitable game partner.
As described above, it is possible to match each user by extracting game attribute data of each user in the server, or to divide users having similar game attribute data into the same group. Fig. 1 illustrates a schematic diagram of an example environment 100 in which various embodiments of the present disclosure can be implemented. As shown in fig. 1, an example environment 100 may include an application server 101, a network 102, and a plurality of terminal devices 103, 104, 105. Data interaction is typically performed between the application server 102 and the network 102, between the network 102 and a plurality of terminal devices 103, 104, 105. In some embodiments, terminal device 103 may be a computer, terminal device 104 may be a PDA, and terminal device 105 may be a smartphone. It is to be understood that the terminal devices 103, 104, 105 may also be other devices capable of communicating with the server 101 via the network 102.
In some embodiments, a user 106 may be operating a computer 103, and behavior attribute data generated by the operation to describe the behavior of the user 106 will be transmitted via the network 102 and stored on the application server 101. Similarly, users 107, 108 may operate PDA 104, smartphone 105, respectively, and the behavior attribute data generated by the operation to describe the behavior of users 107, 108 will also be transmitted via network 102 and stored on application server 101.
With continued reference to fig. 1, in order to increase the entertainment value of the game, game attribute data of the users 106, 107, 108, and the like are generally extracted in the server 101. In a conventional user behavior attribute data management system, game attribute data can be created in the server 101 for the users 106, 107, and 108, respectively. By way of example, the game attribute data may include a game rating, a win ratio, a score, a ranking, or a frequency, duration of play within a predetermined time (e.g., a week or other time interval) of the users 106, 107, and 108 in the respective game.
According to conventional schemes for determining user grouping or matching, the mechanism is primarily to use a rating system such as the erlo rating system for rating, or to sum a plurality of game attribute data of users by weighting to obtain a score, and then to search for users with similar scores for matching. However, operations on attribute data are generally not extensible.
Further, the above-described matching mechanism of a plurality of users is operated based on game attribute data of each user in the game application, but not all users pay attention only to the experience of the battle game. Especially for users with social needs, matching according to the traditional non-scalable matching mechanism may not be satisfactory if the user is more expected to be able to match with other users with similar social attribute data for playing the game. For example, in a case where the user more desires to match with a user who opens a voice setting, a case where the user more desires to match with a user who is different in nature, or a case where the user more desires to match with a user having a close geographical location, or the like, the matching result according to the conventional matching mechanism may not be satisfactory.
According to an embodiment of the present disclosure, a scheme for determining user groupings is provided. In the scheme, different from the traditional operation of weighting and summing the game attribute data of the users to obtain a score and searching the users with similar scores for matching, the scheme provided by the disclosure compares a plurality of behavior attributes in the behavior attribute data of one user with a plurality of corresponding behavior attributes in the behavior attribute data of another user respectively to obtain a plurality of corresponding difference scores, so that various behavior attributes of the difference scores can be expanded, the user matching can be more appropriately performed, and the user experience is improved.
Fig. 2 illustrates a flow chart of a method 200 of determining user groupings in accordance with an embodiment of the present disclosure. The method 200 may be implemented at the application server 101, for example, by a computing device in the application server 101 that is responsible for user behavior management. The actions involved in the method 200 will be described below in conjunction with the example environment 100 shown in FIG. 1.
At 202, behavioral attribute data of a first user (e.g., user 106) and behavioral attribute data of a second user (e.g., user 108) are obtained. In other words, when performing matching operations for a plurality of users 106, 108 waiting for matching, various behavior attribute data of the respective users can be comprehensively considered, so that the matching result satisfies the user's experience in game play as much as possible.
At 204, a plurality of behavior attributes in the behavior attribute data of user 106 are respectively compared to a corresponding plurality of behavior attributes in the behavior attribute data of user 108 to obtain a corresponding plurality of difference scores. In some embodiments, the plurality of behavioral attributes may include a user's voice state setting, gender, geographic location, score, and game level. By way of example, when the voice state of user 106 is set to on and the voice state of user 108 is set to on, the difference score of the voice state settings of both users is 0. When the gender of the user 106 is different from the gender of the user 108, the difference score of the genders of the two users is 0. When the geographic location of user 106 is 10 kilometers to 50 kilometers from the geographic location of user 108, the difference in geographic locations of the two users is scored as 5. When the difference between the score of user 106 and the score of user 108 is less than 50, the difference score between the scores of the two users is 1. When the game level of the user 106 is the same as the game level of the user 108, the difference score of the game levels of the two users is 1.
In some embodiments, such as a difference score between the gender of the two users 106, 108, a difference score between geographic locations, etc., may be implemented by looking up a preset mapping table. As an example, when the two users are different in gender, the difference score in the mapping table may be set to 0; when both users are male, the difference score in the mapping table may be set to 3; and when both users are female, the difference score in the mapping table may be set to 15. It will be appreciated that the above is merely illustrative of one example of a mapping table, and that in other embodiments, different mapping tables may be provided depending on the particular application environment. By respectively obtaining the difference scores of the various behavior attributes, the threshold values of the various attributes can be independently set according to matching requirements in different games. New attributes may also be dynamically and extendably added for matching operations.
At 206, based on the plurality of difference scores, an overall difference score for user 106 and user 108 is determined. In some embodiments, the operation of determining the total difference score may comprise: a plurality of weights associated with the plurality of behavior attributes are obtained, respectively, and a total difference score for the user 106 and the user 108 is determined based on the plurality of difference scores and the plurality of weights. As an example, the weights for voice status setting, gender, geographic location, score, and game level may be set to 1.0, 1.8, 0.8, 0.7, and 0.8, respectively, for a particular game. It is to be understood that the above weights may also be set to other values as desired. The total difference score after weighted summation was 5.5 based on the plurality of difference scores 0, 5, 1 and the plurality of weights 1.0, 1.8, 0.8, 0.7 and 0.8.
At 208, it is determined whether the total difference score is less than or equal to a predetermined value. Preferably, the predetermined value is optionally set to 10. As described above, the total difference score was 5.5, which is the case of being less than or equal to the predetermined value. In this case, the operation flow proceeds to 210. At 210, users 106 and 108 may be divided into the same group. That is, user 106 may be matched to user 108. By the operation of determining the user group, each difference score between users can be compared respectively, thereby increasing the expandability of the grouping operation.
Further, in one embodiment, taking user 106 as an example, to avoid that user 106 waits too long for a match, which affects the game experience, the total difference score between other users found within a predetermined time period and user 106 is increased by a predetermined value. Thus, the time for the user to wait for the matching operation can be reduced, for example, in the case of fewer alternative users.
Fig. 3 illustrates a flow diagram of a method 300 of obtaining behavior attribute data prior to determining user groupings according to an embodiment of the present disclosure. In some embodiments, method 300 is a prior step of method 200. Similar to method 200, method 300 may also be implemented at application server 101, for example, by a computing device in application server 101 that is responsible for user behavior management. The actions involved in the method 300 will be described below in conjunction with the example environment 100 shown in FIG. 1.
At 302, game attribute data of the user 106 in the application server 101 is obtained for the user 106 of the application server 101. The game attribute data describes attribute data of a game behavior of the user 106 in the game provided by the application server 101. In some embodiments, the game attribute data comprises at least any one of: a rank of the user; the winning rate of the user; a score of the user; ranking of users; the frequency of the user's games; and the duration of the user's game.
In some embodiments, to match users with similar game levels and proficiency, users with similar ranking (which substantially reflects game levels) and game frequency (which substantially reflects proficiency), such as user 106 and user 108, may be found among the users 106, 107, and 108 waiting for a match. Therefore, by acquiring the game attribute data of each user in the application server 101, users having close game levels can be matched together for a match, and the winning rate of all users can be kept at about 50%.
It should be understood that since the game is not limited to a one-to-one match of two users, for example, a many-to-many (e.g., five-to-five) match is also possible, the target number of matching users is not limited to 2, but may be 4, 10 or other numbers.
At 304, social attribute data of the user 106 in the application server 101 is obtained. The social attribute data describes attribute data of social behavior of the user 106 between at least one other user of the application server 101. In some embodiments, the social attribute data includes at least any one of: setting a voice state of a user; the number of friends of the user; the gender of the user; and the geographic location of the user.
In some embodiments, in order to match together users who are willing to make friends, users who are different in gender (making friends with opposite sex) and each open voice settings (with a desire to communicate with voice), such as user 106 and user 108, may be sought among users 106, 107, and 108 waiting for a match. Alternatively or additionally, users with more than 100 friends (favoring friends to meet) and less than 5 kilometers in geographic location (facilitating offline meeting) may also be found among users 106, 107, and 108 waiting for a match. Therefore, by acquiring social attribute data of each user in the application server 101, users having similar social willingness can be matched together to play a game, so that the users can make friends while relaxing the game.
At 306, behavior attribute data of the user 106 in the application server 101 is generated based on the acquired game attribute data and social attribute data.
Taking the game "you draw me guess" as an example, the game belongs to a social game and needs to be completed in cooperation with other users. The general rules of the game are: after the game starts, the system provides three alternative words for a user, and the user needs to select a word from the three alternative words to draw a painting composition, namely, a representation of the word is drawn on a screen of the device by a finger, a touch pen or a mouse in a shorthand way. In this game, the entire drawing process is visible to other users, and the other users will guess the word from the drawn picture within a defined time. The guessed user will receive a reward such as a bonus.
In the aspect of game attribute data of the game application, since the game level of the user is not completely associated with the level, total score, ranking, and frequency of game of the user, these attribute data may not be used as a reference condition for matching. In contrast, the user guessing the winning rate of the vocabulary combined with the game duration (keeping a certain winning rate for a period of time) can reflect the game level of the user. Therefore, the winning rate and the game duration of the user can be extracted as the game attribute data in the application server 101.
In terms of game attribute data of the game application, since voice communication is not generally allowed during the game, the voice state may not be set as a reference condition for matching, and only the number of friends, gender and geographic location of the user may be extracted as social attribute data in the application server 101. Therefore, the winning rate and the game duration of the user and the number, the gender and the geographic position of friends of the user can be summarized into the behavior attribute data of the user to be used as the reference conditions of the subsequent matching operation.
Fig. 4 illustrates a flow diagram of a method 400 of obtaining behavior attribute data prior to determining user groupings according to an embodiment of the present disclosure. In some embodiments, method 400 is a prior step of method 200. Similar to method 300, method 400 may also be implemented at application server 101, for example, by a computing device in application server 101 that is responsible for user behavior management. The actions involved in the method 300 will be described below in conjunction with the example environment 100 shown in FIG. 1.
At 402, game attribute data of the user 108 in the application server 101 is obtained for a second user 108 of the application server 101. The game attribute data describes attribute data of a game behavior of the user 108 in the game provided by the application server 101. At 304, social attribute data of the user 108 in the application server 101 is obtained. The social attribute data describes attribute data of social behavior of the user 108 between at least one other user of the application server 101. At 306, behavior attribute data of the user 108 in the application server 101 is generated based on the acquired game attribute data and social attribute data. In other words, referring to fig. 3 and 4, the behavior attribute data summarizes the game attribute data and the social attribute data of the user 106, 107, or 108. When the matching operation is executed for a plurality of users 106, 107 or 108 waiting to be matched, the game attribute and the social attribute of each user can be comprehensively considered, so that the matching result meets the experience of the users in game fight as much as possible and can be friends with other users with similar social willingness.
Fig. 5 illustrates a flow chart of a method 500 of determining user groupings in accordance with an embodiment of the present disclosure. In this embodiment, as shown in FIG. 5, at 502, it is determined whether the total difference score is greater than a predetermined value. When the total difference score for user 106 and user 108 is greater than a predetermined value, proceed to 504. At 504, behavioral attribute data of other users (e.g., user 107) in the application server 101 may be generated. At 506, users 106 and 107 can be divided into the same group. That is, user 106 may be matched to user 107. Through the above operation, the user 106 can be prevented from waiting for the matching too long, so that the game experience is improved.
Fig. 6 illustrates a schematic block diagram of an example device 600 that may be used to implement embodiments of the present disclosure. For example, a computing device performing management functions in the example environment 100 shown in FIG. 1 may be implemented by the device 600. As shown, device 600 includes a Central Processing Unit (CPU)601 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM)602 or loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
Various processes and processes described above, such as method 300, method 400, and/or method 500, may be performed by processing unit 601. For example, in some embodiments, method 300, method 400, and/or method 500 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When loaded into RAM 603 and executed by CPU 601, the computer program may perform one or more of the acts of method 300, method 400, and/or method 500 described above.
The present disclosure may be methods, apparatus, systems, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for carrying out various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (17)
1. A method for determining user groupings, the method comprising:
acquiring behavior attribute data of a first user and behavior attribute data of a second user;
comparing a plurality of behavior attributes in the behavior attribute data of the first user with a corresponding plurality of behavior attributes in the behavior attribute data of the second user, respectively, to obtain a corresponding plurality of difference scores;
determining a total difference score for the first user and the second user based on the plurality of difference scores; and
in response to the total difference score being less than or equal to a predetermined value, the first user and the second user are classified into the same group.
2. The method of claim 1, wherein obtaining behavioral attribute data of the first user comprises:
aiming at the first user, obtaining game attribute data of the first user in an application server, wherein the game attribute data describes attribute data of game behaviors of the first user in a game provided by the application server;
obtaining social attribute data of the first user in the application server, wherein the social attribute data describes the social behavior of the first user between the first user and at least one other user of the application server; and
and generating behavior attribute data of the first user in the application server based on the acquired game attribute data and the acquired social attribute data.
3. The method of claim 1, wherein obtaining behavior attribute data for the second user comprises:
aiming at the second user, obtaining game attribute data of the second user in an application server;
acquiring social attribute data of the second user in the application server; and
and generating behavior attribute data of the second user in the application server based on the acquired game attribute data and social attribute data of the second user.
4. The method of claim 1, wherein determining a total difference score for the first user and the second user comprises:
obtaining a plurality of weights associated with the plurality of behavior attributes, respectively; and
determining a total difference score for the first user and the second user based on the plurality of difference scores and the plurality of weights.
5. The method of claim 1, further comprising:
in response to the total difference score being greater than the predetermined value, generating, for other users of an application server, behavioral attribute data of the other users in the application server; and
grouping the first user and the other users based on the behavior attribute data of the first user and the behavior attribute data of the other users.
6. The method of claim 1, further comprising:
in response to the first user and the other users not being classified into the same group for a predetermined period of time, increasing the predetermined value.
7. The method of claim 1, wherein the social attribute data comprises at least any one of:
setting a voice state of a user;
the number of friends of the user;
the gender of the user; and
the geographic location of the user.
8. The method of claim 1, wherein the game attribute data comprises at least any one of:
a rank of the user;
the winning rate of the user;
a score of the user;
ranking of users;
the frequency of the user's games; and
the length of the user's game.
9. An apparatus for determining user groupings, comprising:
at least one processing unit;
at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, which when executed by the at least one processing unit, cause the apparatus to perform acts comprising:
acquiring behavior attribute data of a first user and behavior attribute data of a second user;
comparing a plurality of behavior attributes in the behavior attribute data of the first user with a corresponding plurality of behavior attributes in the behavior attribute data of the second user, respectively, to obtain a corresponding plurality of difference scores;
determining a total difference score for the first user and the second user based on the plurality of difference scores; and
in response to the total difference score being less than or equal to a predetermined value, the first user and the second user are classified into the same group.
10. The apparatus of claim 9, wherein obtaining behavioral attribute data of the first user comprises:
aiming at the first user, obtaining game attribute data of the first user in an application server, wherein the game attribute data describes attribute data of game behaviors of the first user in a game provided by the application server;
obtaining social attribute data of the first user in the application server, wherein the social attribute data describes the social behavior of the first user between the first user and at least one other user of the application server; and
and generating behavior attribute data of the first user in the application server based on the acquired game attribute data and the acquired social attribute data.
11. The apparatus of claim 9, wherein obtaining behavior attribute data for the second user comprises:
aiming at the second user, obtaining game attribute data of the second user in an application server;
acquiring social attribute data of the second user in the application server; and
and generating behavior attribute data of the second user in the application server based on the acquired game attribute data and social attribute data of the second user.
12. The apparatus of claim 9, wherein determining a total difference score for the first user and the second user comprises:
obtaining a plurality of weights associated with the plurality of behavior attributes, respectively; and
determining a total difference score for the first user and the second user based on the plurality of difference scores and the plurality of weights.
13. The apparatus of claim 9, wherein the actions further comprise:
in response to the total difference score being greater than the predetermined value, generating, for other users of an application server, behavioral attribute data of the other users in the application server; and
grouping the first user and the other users based on the behavior attribute data of the first user and the behavior attribute data of the other users.
14. The apparatus of claim 9, wherein the actions further comprise:
in response to the first user and the other users not being classified into the same group for a predetermined period of time, increasing the predetermined value.
15. The apparatus of claim 9, wherein the social attribute data comprises at least any one of:
setting a voice state of a user;
the number of friends of the user;
the gender of the user; and
the geographic location of the user.
16. The apparatus of claim 9, wherein the game attribute data comprises at least any one of:
a rank of the user;
the winning rate of the user;
a score of the user;
ranking of users;
the frequency of the user's games; and
the length of the user's game.
17. A computer-readable storage medium having computer-readable program instructions stored thereon for performing the method of any of claims 1-8.
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