CN115228091A - Game recommendation method, device, equipment and computer readable storage medium - Google Patents
Game recommendation method, device, equipment and computer readable storage medium Download PDFInfo
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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
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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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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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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
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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
- A63F2300/556—Player lists, e.g. online players, buddy list, black list
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Abstract
The invention discloses a game recommendation method, a game recommendation device, equipment and a computer readable storage medium, wherein the game recommendation method comprises the following steps: acquiring first game behavior data and non-game behavior data of a user, and acquiring second game behavior data of a valid game friend of the user account; generating a game representation of the user account according to the first game behavior data, the non-game behavior data and the second game behavior data; and recommending games to the user account according to the game images. The method and the device recommend the game to the user based on the reference basis of the game data of the user, the game friend game data of the user, the daily preference of the user and the like, so that the game recommendation is more objective and more in line with the interest and hobbies of the user, and the user stickiness of the game platform is increased because the game recommendation is more in line with the social expectation of the user in the game.
Description
Technical Field
The invention belongs to the technical field of software, and particularly relates to a game recommendation method, device and equipment and a computer readable storage medium.
Background
With the continuous development of internet technology and the opening of the thought of people, the game is more and more accepted by people as a leisure and entertainment mode. In order to facilitate the user group to select favorite games among a large number of games, it is common to classify the games, such as role-playing games, action games, adventure games, strategy games, fighting games, shooting games, first-person perspective shooting games, intellectual games, and racing games. However, as the number of games increases, it is difficult for users to find favorite games depending on the category. Although some game platforms exist at present to push games to users, the games are usually in commercial use or recommended to the users according to game popularity, therefore, each game pushed to the users is usually the same, and the push content is single, so that the user stickiness is difficult to generate.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a game recommendation method, a game recommendation device, game recommendation equipment and a computer readable storage medium, and aims to solve the technical problem that the traditional game recommendation method is single in pushed content and difficult to generate user stickiness.
In order to achieve the above object, the present invention provides a game recommendation method, including the steps of:
acquiring first game behavior data and non-game behavior data of a user account, and acquiring second game behavior data of a valid game friend of the user account;
generating a game portrait corresponding to the user account according to the first game behavior data, the non-game behavior data and the second game behavior data;
and recommending games to the user client corresponding to the user account according to the game images.
Further, the step of acquiring the first game behavior data and the non-game behavior data of the user account includes:
acquiring historical game data of the user account to generate the first game behavior data;
acquiring first tour information of the user account on a game platform and second tour information of the user account on a video platform, wherein the first tour information and the second tour information are the non-game behavior data.
Further, the step of obtaining second game behavior data of the valid game friends of the user account includes:
obtaining game friends from a game friend list of the user account;
generating the effective game friends according to the interaction conditions of the user account and the game friends;
and generating the second game behavior data according to the historical game data of the effective game friends.
Further, the first game behavior data includes a first acquired game and a first game duration corresponding to the first acquired game, the second game behavior data includes a second acquired game and a second game duration corresponding to the second acquired game, and the step of generating the game representation corresponding to the user account according to the first game behavior data, the non-game behavior data, and the second game behavior data includes:
generating a first game characteristic of the user account according to the first acquired game, and generating a first weight of the first game characteristic according to the first game duration;
generating a second game feature of the user account according to the second acquired game, and generating a second weight of the second game feature according to the second game duration;
generating a favorite portrait corresponding to the user account according to the first tour information and the second tour information;
and generating the game portrait corresponding to the user account according to the first game characteristic, the first weight, the second game characteristic, the second weight and the preference portrait.
Further, the step of recommending the game to the user client corresponding to the user account according to the game portrait comprises:
acquiring current positioning information of the user account, and generating real-time calibration characteristics of the user account according to the current positioning information;
and recommending games to the user client according to the real-time calibration features and the game representation.
Further, the step of recommending a game to the user client based on the real-time calibration features and the game representation comprises:
generating the matching degree of each game and the game portrait, and generating a first target game list according to the matching degree;
screening the first target game list based on the real-time calibration characteristics to obtain a second target game list;
recommending games to the user client based on the second target game list.
Further, the favorite portrait includes an interest feature and a third weight corresponding to the interest feature, and the step of generating the favorite portrait corresponding to the user account according to the first tour information and the second tour information includes:
generating the interest characteristics of the user account according to the first browsing information and the second browsing information;
generating the total browsing information quantity according to the first browsing information and/or the second browsing information;
and generating the third weight according to the browsing times of the interest characteristics and the total browsing information quantity.
In addition, to achieve the above object, the present invention provides a game recommendation device including:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring first game behavior data and non-game behavior data of a user account and acquiring second game behavior data of a valid game friend of the user account;
the generating module is used for generating a game portrait corresponding to the user account according to the first game behavior data, the non-game behavior data and the second game behavior data;
and the recommending module is used for recommending games to the user client side of the user account according to the game representation.
Further, to achieve the above object, the present invention also provides a game recommendation apparatus including: the game recommendation system comprises a memory, a processor and a game recommendation program stored on the memory and capable of running on the processor, wherein the game recommendation program realizes the steps of the game recommendation method when being executed by the processor.
In addition, to achieve the above object, the present invention also provides a computer-readable storage medium having a game recommendation program stored thereon, which, when executed by a processor, implements the steps of the game recommendation method as described above.
The embodiment of the invention provides a game recommendation method, a game recommendation device, equipment and a computer-readable storage medium, which are used for acquiring first game behavior data and non-game behavior data of a user and acquiring second game behavior data of effective game friends of a user account; generating a game representation of the user account according to the first game behavior data, the non-game behavior data and the second game behavior data; and recommending games to the user account according to the game images. The game is recommended to the user based on the reference basis such as the game data of the user, the game data of the game friends of the user, the daily preference of the user and the like, so that the game recommendation is more objective and more in line with the interests and hobbies of the user, and the user stickiness of the game platform is increased because the recommended game is more in line with the social expectation of the user in the game.
Drawings
FIG. 1 is a schematic diagram of an apparatus for a hardware operating environment in which embodiments of the invention are concerned;
FIG. 2 is a flowchart illustrating a game recommendation method according to a first embodiment of the present invention;
FIG. 3 is a flowchart illustrating a game recommendation method according to a second embodiment of the present invention;
fig. 4 is a flowchart illustrating a game recommendation method according to a third embodiment of the present invention.
The implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, fig. 1 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present invention.
The terminal of the embodiment of the invention can be a PC, and can also be an electronic terminal device with a display function, such as a smart phone, a tablet computer, a game console, a portable computer and the like.
As shown in fig. 1, the apparatus may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. The communication bus 1002 is used to implement connection communication among these components. The user interface 1003 may include a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory such as a disk memory. The memory 1005 may alternatively be a storage device separate from the processor 1001 described previously.
Optionally, the device may also include a camera, RF (Radio Frequency) circuitry, sensors, audio circuitry, wiFi modules, and the like. Such as light sensors, motion sensors, and other sensors, among others. Specifically, the light sensor may include an ambient light sensor that may adjust the brightness of the display screen according to the brightness of ambient light, and a proximity sensor that may turn off the display screen and/or the backlight when the mobile terminal is moved to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally, three axes), detect the magnitude and direction of gravity when the mobile terminal is stationary, and can be used for applications (such as horizontal and vertical screen switching, related games, magnetometer attitude calibration), vibration recognition related functions (such as pedometer and tapping) and the like for recognizing the attitude of the mobile terminal; of course, the mobile terminal may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which are not described herein again.
Those skilled in the art will appreciate that the configuration of the apparatus shown in fig. 1 is not intended to be limiting of the apparatus and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a game recommendation program.
In the device shown in fig. 1, the network interface 1004 is mainly used for connecting to a backend server and performing data communication with the backend server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to call the game recommendation program stored in the memory 1005 and perform the following operations:
acquiring first game behavior data and non-game behavior data of a user account, and acquiring second game behavior data of a valid game friend of the user account;
generating a game portrait corresponding to the user account according to the first game behavior data, the non-game behavior data and the second game behavior data;
and recommending games to the user client corresponding to the user account according to the game images.
Further, the processor 1001 may call the game recommendation program stored in the memory 1005, and further perform the following operations:
the step of acquiring the first game behavior data and the non-game behavior data of the user account comprises the following steps:
obtaining historical game data of the user account to generate the first game behavior data;
and acquiring first tour information of the user account on a game platform and second tour information of the user account on a video platform, wherein the first tour information and the second tour information are the non-game behavior data.
Further, the processor 1001 may call the game recommendation program stored in the memory 1005, and further perform the following operations:
the step of obtaining second game behavior data of the effective game friends of the user account comprises the following steps:
obtaining a game friend from a game friend list of the user account;
generating the effective game friends according to the interaction conditions of the user account and the game friends;
and generating the second game behavior data according to the historical game data of the effective game friends.
Further, the processor 1001 may call the game recommendation program stored in the memory 1005, and further perform the following operations:
the first game behavior data includes a first acquired game and a first game duration corresponding to the first acquired game, the second game behavior data includes a second acquired game and a second game duration corresponding to the second acquired game, and the step of generating a game representation corresponding to the user account according to the first game behavior data, the non-game behavior data, and the second game behavior data includes:
generating a first game feature of the user account according to the first acquired game, and generating a first weight of the first game feature according to the first game duration;
generating a second game characteristic of the user account according to the second acquired game, and generating a second weight of the second game characteristic according to the second game duration;
generating a favorite portrait corresponding to the user account according to the first tour information and the second tour information;
and generating the game portrait corresponding to the user account according to the first game characteristic, the first weight, the second game characteristic, the second weight and the preference portrait.
Further, the processor 1001 may call the game recommendation program stored in the memory 1005, and further perform the following operations:
the step of recommending the game to the user client corresponding to the user account according to the game representation comprises the following steps:
acquiring current positioning information of the user account, and generating real-time calibration characteristics of the user account according to the current positioning information;
and recommending games to the user client according to the real-time calibration features and the game representation.
Further, the processor 1001 may call the game recommendation program stored in the memory 1005, and further perform the following operations:
the step of recommending a game to the user client based on the real-time calibration features and the game representation comprises:
generating the matching degree of each game and the game portrait, and generating a first target game list according to the matching degree;
screening the first target game list based on the real-time calibration characteristics to obtain a second target game list;
recommending games to the user client based on the second target game list.
Further, the processor 1001 may call the game recommendation program stored in the memory 1005, and further perform the following operations:
the favorite portrait comprises an interest feature and a third weight corresponding to the interest feature, and the step of generating the favorite portrait corresponding to the user account according to the first tour information and the second tour information comprises:
generating the interest characteristics of the user account according to the first browsing information and the second browsing information;
generating total browsing information quantity according to the first browsing information and/or the second browsing information;
and generating the third weight according to the browsing times of the interest characteristics and the total browsing information quantity.
Referring to fig. 2, a game recommendation method according to a first embodiment of the present invention includes:
step S10, acquiring first game behavior data and non-game behavior data of a user account, and acquiring second game behavior data of a valid game friend of the user account;
specifically, in this embodiment, the implementation subject is a game platform or an application store, and taking the game platform as an example, the game platform may provide a user with a way to download, purchase, or play a game online, and the user may select a game on the game platform according to a preference. Typically, the game platform establishes an online game community for the players, and the game players can communicate with the game center in the game community. In addition, the game platform will record the game behavior and non-game behavior of the player on the game platform, for example, the game behavior is that the user plays a certain game in a certain time period and the duration of the game; and recording information of the non-game behavior users visiting in the game community of the game platform. The user account is an account used by the user on the game platform and is used for determining personal information of the user, and it can be understood that first game behavior, non-game behavior data and the like of the user account are actually made by the user corresponding to the account, so that the user is described in the following description in place of the user account.
Further, historical game data of the user account is acquired to generate the first game behavior data; and acquiring first tour information of the user account on a game platform and second tour information of the user account on a video platform, wherein the first tour information and the second tour information are the non-game behavior data.
Specifically, the historical game data is data generated when a user plays a game on the game platform, and the historical game data is provided with a time window, and if the historical game data is game data which is generated when the user is in a month, on one hand, reference data of the push game can be changed according to the change of the preference of the user, and on the other hand, the amount of the game data is also reduced. And generating first game behavior data according to the game data of the user in the last month, wherein the first game behavior data comprises games played by the user and the game duration of each game. In addition, the game platform also acquires the tour information of the user in the game community. In a game community, the form of communication between players is usually a message issued by a player with a subject name under which other players can leave messages for communication. And the first tour data is the name of the subject that the user has browsed. In addition, the second tour information of the user on the video platform can be obtained through an API (Application Program Interface) of the video platform. It should be noted that, in general, the video platform itself generates the user interest or preference features, such as movies, music, animation, travel, or military affairs, according to the browsing information of the user. Therefore, the second browsing information can be directly the user's favorite features generated by the video platform. It is understood that the second browsing information is not based on the favorite features generated by the game itself, and therefore, in the embodiment, the reference used in pushing the game is more diversified, and the constraint of the game itself is broken.
Further, obtaining a game friend from a game friend list of the user account; generating the effective game friends according to the interaction conditions of the user account and the game friends; and generating the second game behavior data according to the historical game data of the effective game friends.
In particular, friends can be added between players in the game platform, and the added friends are placed in a friend list. Further, in some cases, friends in the user's friends list are not actively added by the user, e.g., the game platform may synchronize third party social platform friend information, adding friends of the social platform as game friends into the friendly list. Therefore, in order to ensure the accuracy of game recommendation, in this embodiment, the game friend list is further filtered to generate effective game friends. Specifically, the effective game friends are generated according to the interaction times between the users and the game friends, wherein the interaction times include but are not limited to the times of playing games together and the times of sending messages to each other. And taking the game friends with the interaction times larger than the preset threshold value as effective game friends. Similarly, second game behavior data is generated according to historical game data of effective game friends. It should be noted that the second game behavior data is also used as the first game behavior data of the valid game friend itself, and therefore, when the valid game friend itself has generated the first game behavior data, the first game behavior data of the valid game friend can be directly acquired as the second game behavior data of the user account. It can be understood that, in this embodiment, the game data of the game friends that frequently interact with the user is used as one of the bases for pushing the game to the user, so that the users and the game friends have more games, and the interaction channels between the users and the game friends are increased, thereby increasing the user stickiness of the game platform.
Step S20, generating a game portrait corresponding to the user account according to the first game behavior data, the non-game behavior data and the second game behavior data;
further, the first game behavior data includes a first acquired game and a first game duration corresponding to the first acquired game, the second game behavior data includes a second acquired game and a second game duration corresponding to the second acquired game, and the step of generating the game representation of the user account according to the first game behavior data, the non-game behavior data, and the second game behavior data includes: generating a first game characteristic of the user account according to the first acquired game, and generating a first weight of the first game characteristic according to the first game duration; generating a second game characteristic of the user account according to the second acquired game, and generating a second weight of the second game characteristic according to the second game duration; generating a favorite portrait corresponding to the user account according to the first tour information and the second tour information; and generating the game portrait corresponding to the user account according to the first game characteristic, the first weight, the second game characteristic, the second weight and the preference portrait.
Further, the favorite portrait includes an interest feature and a third weight corresponding to the interest feature, and the step of generating the favorite portrait corresponding to the user account according to the first tour information and the second tour information includes: generating the interest characteristics of the user account according to the first browsing information and the second browsing information; generating total browsing information quantity according to the first browsing information and/or the second browsing information; and generating the third weight according to the browsing times of the interest characteristics and the total browsing information quantity.
Specifically, the first game behavior includes a first acquired game, where the first acquired game is a game that the user downloads, purchases or plays at the game platform (i.e., a game that has been included by the user at the game platform). The first game action further comprises the game duration of each acquired game, namely the first game duration, of the user. Similarly, the second game activity data source is a valid game friend and includes a second acquired game and a second game duration. The game tags are played for different games, and the game tags may include categories of games, elements of games (such as those related to animations or movies, etc.), game vendors, and the like. It should be noted that, a game tag is an inherent objective attribute of a game, and a technician can play a corresponding game tag for the game according to actual conditions. The game tags of the acquired games are used as corresponding game features, the game tags from the users are used as first game features, and the game tags from the effective game friends are used as second game features. A first weight of the first game feature is generated based on a first game duration corresponding to the first game feature. For example, the user has played two games in the last month, namely game 1 (game duration is a) and game 2 (game duration is b), the game features of game 1 include feature 1, feature 2 and feature 3, and the game features of game 2 include feature 2 and feature 4, so that the first game features of the user are feature 1, feature 2, feature 3 and feature 4, and the corresponding first weights include feature 1 weight a/(a + b), feature 2 weight (a + b)/(a + b), feature 3 weight a/(a + b) and feature 4 weight b/(a + b). Similarly, the second game feature and the second weight may refer to the above generation process and are not described herein again. And further generating a favorite portrait of the user according to the first tour information and the second tour information. And (3) performing data preprocessing on the subject names in the first tour information, for example, extracting a keyword cartoon 1 from a subject name of the same person as cartoon 1, wherein the number of tours of the cartoon 1 is +1. If the cartoon 1 is the interest feature 1, the weight of the interest feature 1 is the number of times of appearance of the cartoon 1/the total browsing information quantity, and the total browsing information quantity obtains the number of all theme name items. Similarly, the second browsing information includes the interest features of the user generated by the video platform, and the weight generation mode of each interest feature is as follows: and interest feature browsing times/total browsing information quantity, wherein the total browsing information quantity is the video browsing quantity. In addition, the total browsing information amount can also be obtained by adding the video browsing amount and the number of the subject name items. The favorite portrait is composed of each interest feature and the weight value corresponding to each interest feature. It can be understood that, in the embodiment, the interest of the user in life is taken as one of the factors of the game recommendation, so that the game recommendation is more in line with the user interest. Mapping the interest feature to a game feature according to a preset mapping rule, for example, the interest feature is cartoon 1, and if game 1 is adapted based on cartoon 1, game 1 will be labeled with cartoon 1, so cartoon 1 can be directly used as the game feature. For those who cannot directly be used as game features, for example, the interest feature is military, the military is mapped to shooting games or strategy games, and the specific mapping rules can be made by technicians. The game image is composed of game features and weights corresponding to the game features. It should be noted that different weights may exist for the same game feature, and for example, if the source user obtains the weight 1, the valid game friends obtains the weight 2, and the favorite portrait obtains the weight 3, the weight with the largest weight value is taken as the weight corresponding to the game feature.
And step S30, recommending games to the user client corresponding to the user account according to the game images.
Specifically, when the game tag of the game to be recommended is acquired, it can be understood that the game representation is composed of game features and weights corresponding to the game features, where the game features correspond to (are the same as) the game tag, and the matching degree is generated according to the weights corresponding to the game tags of the game to be recommended in the game representation. For example, the game tag of the game to be recommended includes game features 5 and 6, and the game image includes the feature 5 corresponding to the weight 5, but the game image does not include the game feature 6, so that the matching degree between the generated game to be recommended and the game image is the weight 5, and if the game feature 6 is included and the weight 6 is corresponding to the weight 6, the matching degree between the game to be recommended and the game image is: weight 5+ weight 6. Generating the matching degree of each game, generating a game recommendation list according to the sequence of the matching degrees of the games from top to bottom, and displaying the games on a user client according to the sequence in the recommendation list, wherein the client is the client of the game platform on the equipment used by the user.
In this embodiment, first game behavior data and non-game behavior data of a user are acquired, and second game behavior data of a valid game friend of the user account is acquired; generating a game representation of the user account according to the first game behavior data, the non-game behavior data and the second game behavior data; and recommending games to the user account according to the game representation. The game is recommended to the user based on the reference basis such as the game data of the user, the game data of the game friends of the user, the daily preference of the user and the like, so that the game recommendation is more objective and more accordant with the interest and hobbies of the user, and the user stickiness of the game platform is increased because the recommended game is more accordant with the social expectation of the user in the game.
Further, referring to fig. 3, based on the second embodiment of the game recommendation method of the present invention proposed by the first embodiment of the game recommendation method of the present invention:
the step S30 includes:
step S31, acquiring current positioning information of the user account, and generating real-time calibration characteristics of the user account according to the current positioning information;
and step S32, recommending games to the user client according to the real-time calibration features and the game portrait.
Specifically, current positioning information of a user is obtained, and a scene where the user account is located is determined according to the current positioning information, wherein the scene comprises an outgoing scene or a home scene, a game type corresponding to the outgoing scene is a short-time game, and a game type corresponding to the home scene is a long-time game. Thus, the real-time calibration feature includes a short-time game and a long-time game, i.e., the short-time game and the long-time game are also game features. And deleting games which are not in accordance with the real-time calibration characteristics in the generated game recommendation list to generate a new game recommendation list, so that the game recommendation is more in accordance with the current environment of the user.
Further, referring to fig. 4, a third embodiment of the game recommendation method of the present invention based on the second embodiment of the game recommendation method of the present invention:
the step S32 includes:
step S321, generating the matching degree of each game and the game portrait, and generating a first target game list according to the matching degree;
step S322, filtering the first target game list based on the first acquired game to generate a new first target game list;
step S323, screening the first target game list based on the real-time calibration characteristics to obtain a second target game list;
step S324, recommending a game to the user client based on the second target game list.
Specifically, the matching degree of each game is generated, and the first target game list is generated in the order of the matching degree of each game from top to bottom. Further, in order to avoid repeated recommendation of game features which are already owned by the user, the games which are already owned by the user in the first target game list are deleted, and a new first target game list is obtained. And then, screening the first target game list based on the real-time calibration feature, for example, if the real-time calibration feature is a long-time game, generating a second target game list for the game reservation of the long-time game in the first target game list.
In addition, an embodiment of the present invention further provides a game recommendation device, where the game recommendation device includes:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring first game behavior data and non-game behavior data of a user account and acquiring second game behavior data of a valid game friend of the user account;
the generating module is used for generating a game portrait corresponding to the user account according to the first game behavior data, the non-game behavior data and the second game behavior data;
and the recommending module is used for recommending games to the user client side of the user account according to the game representation.
Optionally, the obtaining module is further configured to:
acquiring historical game data of the user account to generate the first game behavior data;
and acquiring first tour information of the user account on a game platform and second tour information of the user account on a video platform, wherein the first tour information and the second tour information are the non-game behavior data.
Optionally, the obtaining module is further configured to:
obtaining game friends from a game friend list of the user account;
generating the effective game friends according to the interaction conditions of the user account and the game friends;
and generating the second game behavior data according to the historical game data of the effective game friends.
Optionally, the first game behavior data includes a first acquired game and a first game duration corresponding to the first acquired game, the second game behavior data includes a second acquired game and a second game duration corresponding to the second acquired game, and the generating module is further configured to:
generating a first game feature of the user account according to the first acquired game, and generating a first weight of the first game feature according to the first game duration;
generating a second game characteristic of the user account according to the second acquired game, and generating a second weight of the second game characteristic according to the second game duration;
generating a favorite portrait corresponding to the user account according to the first tour information and the second tour information;
and generating the game portrait corresponding to the user account according to the first game characteristic, the first weight, the second game characteristic, the second weight and the preference portrait.
Optionally, the recommendation module is further configured to:
acquiring current positioning information of the user account, and generating real-time calibration characteristics of the user account according to the current positioning information;
and recommending games to the user client according to the real-time calibration features and the game representation.
Optionally, the recommendation module is further configured to:
generating the matching degree of each game and the game portrait, and generating a first target game list according to the matching degree;
screening the first target game list based on the real-time calibration characteristics to obtain a second target game list;
recommending games to the user client based on the second target game list.
Optionally, the preference profile includes an interest feature and a third weight corresponding to the interest feature, and the generating module is further configured to:
generating the interest characteristics of the user account according to the first browsing information and the second browsing information;
generating the total browsing information quantity according to the first browsing information and/or the second browsing information;
and generating the third weight according to the browsing times of the interest characteristics and the total browsing information quantity.
The game recommendation device provided by the invention adopts the new game recommendation method in the embodiment, and solves the technical problem that the current game recommendation method is single in push content and difficult to generate user stickiness. Compared with the prior art, the new game recommendation device provided by the embodiment of the invention has the same beneficial effects as the new game recommendation method provided by the embodiment, and other technical characteristics of the new game recommendation device are the same as those disclosed by the embodiment method, which are not repeated herein.
In addition, an embodiment of the present invention further provides a game recommendation device, where the game recommendation device includes: the game recommendation system comprises a memory, a processor and a game recommendation program stored on the memory and capable of running on the processor, wherein the game recommendation program realizes the steps of the game recommendation method when being executed by the processor.
The specific implementation of the new game recommendation device of the present invention is substantially the same as that of each of the above new game recommendation methods, and is not described herein again.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where a game recommendation program is stored, and when executed by a processor, the game recommendation program implements the steps of the game recommendation method as described above.
The specific implementation manner of the medium of the present invention is basically the same as that of each embodiment of the new game recommendation method, and is not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or system comprising the element.
The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments.
Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method of the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solution of the present invention essentially or contributing to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above and includes several instructions for enabling a terminal device (such as a mobile phone, a computer, a server, a game console, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A game recommendation method, characterized in that the game recommendation method comprises the following steps:
acquiring first game behavior data and non-game behavior data of a user account, and acquiring second game behavior data of a valid game friend of the user account;
generating a game portrait corresponding to the user account according to the first game behavior data, the non-game behavior data and the second game behavior data;
and recommending games to the user client corresponding to the user account according to the game images.
2. The game recommendation method of claim 1, wherein the step of obtaining the first game activity data and the non-game activity data for the user account comprises:
obtaining historical game data of the user account to generate the first game behavior data;
acquiring first tour information of the user account on a game platform and second tour information of the user account on a video platform, wherein the first tour information and the second tour information are the non-game behavior data.
3. The game recommendation method of claim 2, wherein the step of obtaining second game behavior data for active game friends of the user account comprises:
obtaining game friends from a game friend list of the user account;
generating the effective game friends according to the interaction conditions of the user account and the game friends;
and generating the second game behavior data according to the historical game data of the effective game friends.
4. A game recommendation method according to claim 3, wherein the first game play data includes a first captured game and a first game play duration corresponding to the first captured game, the second game play data includes a second captured game and a second game play duration corresponding to the second captured game, and the step of generating a game representation corresponding to the user account based on the first game play data, the non-game play data and the second game play data comprises:
generating a first game feature of the user account according to the first acquired game, and generating a first weight of the first game feature according to the first game duration;
generating a second game feature of the user account according to the second acquired game, and generating a second weight of the second game feature according to the second game duration;
generating a favorite portrait corresponding to the user account according to the first tour information and the second tour information;
and generating the game portrait corresponding to the user account according to the first game characteristic, the first weight, the second game characteristic, the second weight and the preference portrait.
5. The game recommendation method of claim 4, wherein the step of recommending a game to a user client corresponding to the user account based on the game representation comprises:
acquiring current positioning information of the user account, and generating real-time calibration characteristics of the user account according to the current positioning information;
and recommending games to the user client according to the real-time calibration features and the game representation.
6. A game recommendation method according to claim 5, wherein said step of recommending a game to said user client based on said real-time calibration characteristics and said game representation comprises:
generating the matching degree of each game and the game portrait, and generating a first target game list according to the matching degree;
screening the first target game list based on the real-time calibration characteristics to obtain a second target game list;
recommending games to the user client based on the second target game list.
7. A game recommendation method as recited in claim 6, wherein said preference profile includes an interest feature and a third weight corresponding to said interest feature, and said step of generating said preference profile corresponding to said user account based on said first tour information and said second tour information comprises:
generating the interest characteristics of the user account according to the first browsing information and the second browsing information;
generating the total browsing information quantity according to the first browsing information and/or the second browsing information;
and generating the third weight according to the browsing times of the interest characteristics and the total browsing information quantity.
8. A game recommendation device, characterized in that the game recommendation device comprises:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring first game behavior data and non-game behavior data of a user account and acquiring second game behavior data of a valid game friend of the user account;
the generating module is used for generating a game portrait corresponding to the user account according to the first game behavior data, the non-game behavior data and the second game behavior data;
and the recommending module is used for recommending games to the user client side of the user account according to the game representation.
9. A game recommendation device characterized in that it comprises: a memory, a processor and a game recommendation program stored on the memory and executable on the processor, the game recommendation program when executed by the processor implementing the steps of the game recommendation method of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a game recommendation program which, when executed by a processor, implements the steps of the game recommendation method according to any one of claims 1 to 7.
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