KR20210060948A - Apparatus and method for recommending game - Google Patents

Abstract

Disclosed are a device and a method for recommending a game. According to an embodiment of the present invention, a game recommendation method includes the steps of: calculating the degree of the similarity between a user and the one or more other users, respectively, based on the game rating information of the user and one or more other users; calculating an expected rating for each of a plurality of recommended candidate games based on similarity; and determining a recommended candidate game having the highest expected rating among the plurality of recommended candidate games as the recommended game to be recommended to the user. The present invention can intuitively provide a taste analysis result for the game which has not yet been played.

Description

Game recommendation device and method {APPARATUS AND METHOD FOR RECOMMENDING GAME}

Disclosed embodiments relate to game recommendation technology.

As games emerge as important cultural contents and the demand for games increases rapidly, numerous genres of games are continuously released through various platforms. Accordingly, the necessity of a service capable of grasping in advance which game is suitable for one's taste among previously released games and games to be released has emerged.

Conventional game recommendation services reflect this user's request and provide customized game recommendation services based on questionnaire information and game play reviews obtained from users, but a recommendation service that can be continuously enhanced by encompassing various game providing platforms. There is a limit to what is provided.

(Patent Document 1) Korean Laid-Open Patent Publication No. 10-2009-0095283 (2009.09.09.)

The disclosed embodiments are for recommending games by encompassing various game providing platforms.

In addition, the disclosed embodiments are for providing a game recommendation service capable of continuous advancement.

A game recommendation method according to an embodiment includes calculating a similarity between the user and the one or more other users, respectively, based on the user's game-specific rating information and one or more other user's game-specific rating information, based on the similarity. And calculating an expected rating for each of a plurality of recommended candidate games, and determining a recommended candidate game having the highest predicted rating among the plurality of recommended candidate games as a recommended game to be recommended to the user.

A game recommendation method according to an embodiment includes the steps of acquiring the user's preferred platform information and the user's preferred genre information, and determining the plurality of recommended candidate games based on the preferred platform information and the preferred genre information. It may further include.

The plurality of recommended candidate games may include games that are serviced through a platform corresponding to the preferred platform information among a plurality of previously registered games and classified into a genre corresponding to the preferred genre information.

The at least one other user may be a user who has a play history for at least one game among the plurality of recommended candidate games and a game in which the play history of the user exists.

The similarity may be a Pearson correlation coefficient between the user's rating information for each game and the one or more other users' rating information for each game.

The calculating of the expected rating includes calculating a weighted sum of the rating information for each game of the one or more other users by using the similarity as a weight, and the weighted sum as a sum of the similarity. It may include the step of determining the divided value as the expected score.

The game recommendation method according to an embodiment may further include providing game recommendation information including a name and an expected rating of the recommended game to the user.

A game recommendation device according to an embodiment includes a similarity calculation unit for calculating a similarity between the user and the at least one other user based on the user's game-specific rating information and one or more other user's game-specific rating information, and the similarity A prediction rating calculation unit that calculates an expected rating for each of a plurality of recommended candidate games based on the prediction rating calculation unit, and a recommended game determination that determines a recommended candidate game having the highest predicted rating among the plurality of recommended candidate games as a recommended game to be recommended to the user. Includes wealth.

The game recommending device according to an embodiment determines the plurality of recommended candidate games based on the user's preferred platform information and the user's preferred genre information, and an information acquisition unit that obtains the user's preferred platform information and the preferred genre information. It may further include a candidate decision unit.

The plurality of recommended candidate games may include games that are serviced through a platform corresponding to the preferred platform information among a plurality of previously registered games and classified into a genre corresponding to the preferred genre information.

The at least one other user may be a user who has a play history for at least one game among the plurality of recommended candidate games and a game in which the play history of the user exists.

The similarity may be a Pearson correlation coefficient between the user's rating information for each game and the one or more other users' rating information for each game.

The predicted rating calculation unit includes a weighted sum calculation unit that calculates a weighted sum of the rating information for each game of the one or more other users by using the similarity as a weight, and the weighted sum as a sum of the similarity. It may include an expected rating determination unit that determines the divided value as the expected rating.

The apparatus for recommending a game according to an embodiment may further include an information providing unit providing game recommendation information including a name and an expected rating of the recommended game to the user.

According to the disclosed embodiments, a taste analysis result for a game that has not yet been played may be intuitively provided by calculating an expected rating of a game based on similarity between users.

In addition, according to the disclosed embodiments, by primarily filtering the recommended candidate games based on the user's preferred platform information, it is possible to provide a practical game recommendation service to users who use games through various platforms.

In addition, according to the disclosed embodiments, by calculating an expected rating using a weighted sum of rating information for each game of other users, a reliable game recommendation service can be provided as the number of other users increases.

1 is a block diagram illustrating a device for recommending a game according to an exemplary embodiment;
2 is a block diagram for explaining a game recommendation device according to another embodiment
3 is a block diagram illustrating an expected score calculation unit according to an exemplary embodiment;
4 is a flowchart illustrating a method for recommending a game according to an exemplary embodiment
5 is a flowchart illustrating a game recommendation method according to another embodiment
6 is a block diagram illustrating and describing a computing environment including a computing device suitable for use in example embodiments.

Hereinafter, a specific embodiment will be described with reference to the drawings. The following detailed description is provided to aid in a comprehensive understanding of the methods, devices, and/or systems described herein. However, this is only an example, and the disclosed embodiments are not limited thereto.

In describing the embodiments, if it is determined that a detailed description of related known technologies may unnecessarily obscure the subject matter of the disclosed embodiments, a detailed description thereof will be omitted. In addition, terms to be described later are terms defined in consideration of functions in the disclosed embodiments and may vary according to the intention or custom of users or operators. Therefore, the definition should be made based on the contents throughout the present specification. The terms used in the detailed description are only for describing the embodiments, and should not be limiting. Unless explicitly used otherwise, expressions in the singular form include the meaning of the plural form. In the present description, expressions such as "comprising" or "feature" are intended to refer to certain features, numbers, steps, actions, elements, some or combination thereof, and one or more It should not be construed to exclude the presence or possibility of other features, numbers, steps, actions, elements, any part or combination thereof.

Hereinafter, "game" means software that one or more gamers can enjoy using an input device mounted on or connected to a gaming device. Usually, the player character is directly manipulated or the movement of objects in the game is manipulated, and genres are also classified into role-playing, adventure, and arcade.

Hereinafter, "game recommendation" refers to an act of collecting data including ratings for games used by users, and recommending games that suit users' tastes based on the collected data.

Hereinafter, the term "platform" refers to a means for driving a game, and may be classified into, for example, a web, a console, and a mobile application.

Hereinafter, "genre" refers to the type of game classified based on the system in the game, for example, action, role-playing, puzzle, adventure, It can be subdivided into simulation, sports, shooting, arcade, etc.

1 is a block diagram illustrating a game recommendation device 100 according to an exemplary embodiment. Referring to FIG. 1, a game recommendation device 100 according to an embodiment includes a similarity calculation unit 110, an expected rating calculation unit 120, and a recommended game determination unit 130.

The similarity calculation unit 110 calculates the similarity between the user and one or more other users, respectively, based on the user's game-specific rating information and one or more other users' game-specific rating information.

According to an embodiment, the at least one other user may be a user who has a play history for at least one game among a plurality of recommended candidate games and a game in which the play history of the user exists.

According to an embodiment, the plurality of recommended candidate games may mean a set of one or more games that satisfy a condition preset by the user among games not played by the user.

In this case, the conditions preset by the user may be a type of a preferred platform, a preferred genre, a game release year, a game price, a game age, etc., but are not limited thereto.

Specifically, the plurality of recommended candidate games may include games that are serviced through a platform corresponding to the preferred platform information among a plurality of previously registered games and classified into a genre corresponding to the preferred genre information.

According to an embodiment, the similarity may be a Pearson correlation coefficient between the user's rating information for each game and one or more other users' rating information for each game.

Specifically, the Pearson correlation coefficient means a value having a value of -1 or more and less than 1 by quantifying the correlation between two variables. More specifically, the Pearson correlation coefficient means a value obtained by dividing the covariance of two variables by the product of the standard deviation of each of the two variables.

According to an embodiment, a similarity between the user's rating information for each game and the specific other user's rating information for each game may be calculated by Equation 1 below.

[Equation 1]

는 사용자의 n개 중 i번째 게임에 대한 평점 정보,

는 사용자의 n개의 게임 별 평점 정보의 평균 값,

는 특정 타 사용자의 n개 중 i번째 게임에 대한 평점 정보,

는 특정 타 사용자의 n개의 게임 별 평점 정보의 평균 값을 나타낸다.In this case, n is the number of games in which rating information for both a user and a specific other user exists,

Is the rating information for the i-th game out of n users,

Is the average value of the user's n game-star rating information,

Is the rating information for the i-th game out of n of specific other users,

Represents the average value of rating information for n games of a specific other user.

The predicted rating calculation unit 120 calculates an expected rating for each of the plurality of recommended candidate games based on the similarity.

According to an embodiment, the predicted rating refers to a rating of 0 or more and 5 or less expected for a recommended candidate game that the user has not yet played, and the predicted rating calculation unit 120 provides the predicted rating to the user so that the corresponding game is It can intuitively quantify and show whether it suits the user's taste. Hereinafter, it will be described in detail with reference to FIG. 3.

The recommended game determination unit 130 determines a recommended candidate game having the highest predicted rating among the plurality of recommended candidate games as a recommended game to be recommended to a user.

According to an embodiment, the game recommendation device 100 may further include an information providing unit (not shown) that provides game recommendation information including a name of a recommended game and an expected rating to a user.

Specifically, the information providing unit (not shown) may provide game recommendation information including an illustration of a recommended game, a play screen, a name, an expected rating, and a required specification to the user, but is not limited thereto.

2 is a block diagram illustrating a game recommendation device 200 according to another embodiment. Referring to FIG. 2, a game recommendation device 200 according to another embodiment includes an information acquisition unit 210, a candidate determination unit 220, a similarity calculation unit 110, an expected rating calculation unit 120, and a recommended game determination. Includes section 130. Components corresponding to the components in the above embodiment described with reference to FIG. 1 perform the same or similar functions as those described in the above embodiment, so a more detailed description thereof will be omitted.

The information acquisition unit 210 acquires information on a user's preferred platform and information on a user's preferred genre.

According to an embodiment, the information acquisition unit 210 may be a component in the game recommendation device 200 that acquires the user's preferred platform information and the user's preferred genre information from the user's terminal, but is not limited thereto. It may be a server independently arranged outside the recommendation device 200.

The candidate determination unit 220 determines a plurality of recommended candidate games based on the preferred platform information and preferred genre information.

According to an embodiment, the candidate determination unit 220 recommends a game that is serviced through a platform corresponding to the preferred platform information among games in a plurality of previously registered games and classified into a genre corresponding to the preferred genre information. It can be judged as.

In this case, information on a plurality of previously registered game lists may be stored in a database (not shown) that stores game information separate from the game recommendation device 200, but is not limited thereto, and a corresponding database (not shown) ) May be disposed inside the game recommendation device 200.

3 is a block diagram illustrating the predicted score calculation unit 120 according to an exemplary embodiment. Referring to FIG. 3, the expected score calculation unit 120 according to an embodiment may include a weighted sum calculation unit 122 and an expected score determination unit 124.

The weighted sum calculation unit 122 calculates a weighted sum of rating information for each game of one or more other users by using the similarity as a weight.

Specifically, the weighted sum calculation unit 122 selects a specific game from among the recommended candidate games, and calculates a weighted sum of rating information for each game of one or more other users for the specific game.

In this case, the weighted sum means that when there are a plurality of data vectors, a weight value corresponding to each element of the data vector is multiplied and then the multiplication results are summed again.

The predicted rating determination unit 124 determines a value obtained by dividing the weighted sum by the sum of similarities as the predicted rating.

According to an embodiment, the expected rating determination unit 124 may calculate an expected rating for a specific game by Equation 2 below.

[Equation 2]

는 사용자와 n명 중 i번째 타 사용자 간의 유사도,

는 n명 중 i번째 타 사용자의 특정 게임에 대한 평점 정보를 나타낸다.In this case, n is the number of one or more other users who provided rating information when calculating similarity,

Is the similarity between the user and the i-th other user out of n,

Represents rating information for a specific game of the i-th other user among n players.

4 is a flowchart 400 illustrating a method of recommending a game according to an exemplary embodiment. The method illustrated in FIG. 4 may be performed, for example, by the game recommendation device 100 described above.

Referring to FIG. 4, first, the game recommendation device 100 calculates a degree of similarity between a user and one or more other users, respectively, based on the user's game-specific rating information and one or more other user's game-specific rating information (410).

Thereafter, the game recommendation device 100 calculates an estimated rating for each of the plurality of recommended candidate games based on the similarity (420).

Thereafter, the game recommendation device 100 determines a recommended candidate game with the highest predicted rating among the plurality of recommended candidate games as a recommended game to be recommended to the user (430).

In the illustrated flowchart, the method is described by dividing the method into a plurality of steps, but at least some of the steps are performed in a different order, combined with other steps, performed together, omitted, divided into detailed steps, or not shown. One or more steps may be added and performed.

5 is a flowchart 500 for explaining a game recommendation method according to another embodiment. The method illustrated in FIG. 5 may be performed, for example, by the game recommendation device 200 described above.

Referring to FIG. 5, first, the game recommendation device 200 acquires information on a user's preferred platform and information on a user's preferred genre (510).

Thereafter, the game recommendation device 200 determines a plurality of recommended candidate games based on the preferred platform information and the preferred genre information (520).

Thereafter, steps 530 to 550 correspond to steps 410 to 430 in the above embodiment described with reference to FIG. 4, so a more detailed description thereof will be omitted.

In the illustrated flowchart, the method is described by dividing the method into a plurality of steps, but at least some of the steps are performed in a different order, combined with other steps, performed together, omitted, divided into detailed steps, or not shown. One or more steps may be added and performed.

6 is a block diagram illustrating and describing a computing environment 10 including a computing device suitable for use in example embodiments. In the illustrated embodiment, each component may have different functions and capabilities in addition to those described below, and may include additional components in addition to those described below.

The illustrated computing environment 10 includes a computing device 12. In an embodiment, the computing device 12 may be the game recommendation device 100 according to an embodiment. In addition, the computing device 12 may be a game recommendation device 200 according to another embodiment.

The computing device 12 includes at least one processor 14, a computer-readable storage medium 16 and a communication bus 18. The processor 14 may cause the computing device 12 to operate in accordance with the aforementioned exemplary embodiments. For example, the processor 14 may execute one or more programs stored in the computer-readable storage medium 16. The one or more programs may include one or more computer-executable instructions, and the computer-executable instructions are configured to cause the computing device 12 to perform operations according to an exemplary embodiment when executed by the processor 14 Can be.

The computer-readable storage medium 16 is configured to store computer-executable instructions or program code, program data, and/or other suitable form of information. The program 20 stored in the computer-readable storage medium 16 includes a set of instructions executable by the processor 14. In one embodiment, the computer-readable storage medium 16 includes memory (volatile memory such as random access memory, nonvolatile memory, or a suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash It may be memory devices, other types of storage media that can be accessed by the computing device 12 and store desired information, or a suitable combination thereof.

The communication bus 18 interconnects the various other components of the computing device 12, including the processor 14 and computer readable storage medium 16.

Computing device 12 may also include one or more input/output interfaces 22 and one or more network communication interfaces 26 that provide interfaces for one or more input/output devices 24. The input/output interface 22 and the network communication interface 26 are connected to the communication bus 18. The input/output device 24 may be connected to other components of the computing device 12 through the input/output interface 22. The exemplary input/output device 24 includes a pointing device (such as a mouse or trackpad), a keyboard, a touch input device (such as a touch pad or a touch screen), a voice or sound input device, and various types of sensor devices and/or a photographing device. Input devices and/or output devices such as display devices, printers, speakers, and/or network cards. The exemplary input/output device 24 may be included in the computing device 12 as a component constituting the computing device 12, and may be connected to the computing device 102 as a separate device distinct from the computing device 12. May be.

Meanwhile, an embodiment of the present invention may include a program for performing the methods described in the present specification on a computer, and a computer-readable recording medium including the program. The computer-readable recording medium may include a program command, a local data file, a local data structure, or the like alone or in combination. The media may be specially designed and configured for the present invention, or may be commonly used in the field of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and specially configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Includes hardware devices. Examples of the program may include not only machine language codes such as those produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.

Although the exemplary embodiments of the present invention have been described in detail above, those of ordinary skill in the art to which the present invention pertains will understand that various modifications can be made to the above-described embodiments without departing from the scope of the present invention. . Therefore, the scope of the present invention is limited to the described embodiments and should not be determined, and should not be determined by the claims to be described later, but also by the claims and equivalents.

10: computing environment
12: computing device
14: processor
16: computer readable storage medium
18: communication bus
20: program
22: input/output interface
24: input/output device
26: network communication interface
100, 200: game recommendation device
110: similarity calculation unit
120: Estimated GPA calculation unit
122: weighted sum calculation unit
124: Estimated GPA determination unit
130: Recommended game decision section
210: information acquisition unit
220: Candidate decision section

Claims (14)

Calculating a similarity between the user and the one or more other users, respectively, based on the user's game-specific rating information and one or more other users' game-specific rating information;
Calculating an expected rating for each of a plurality of recommended candidate games based on the similarity; And
And determining a recommended candidate game having the highest predicted rating among the plurality of recommended candidate games as a recommended game to be recommended to the user.
The method according to claim 1,
Acquiring the user's preferred platform information and the user's preferred genre information; And
Determining the plurality of recommended candidate games based on the preferred platform information and the preferred genre information.
The method according to claim 2,
The plurality of recommendation candidate games includes games that are serviced through a platform corresponding to the preferred platform information among a plurality of pre-registered games and classified into a genre corresponding to the preferred genre information.
The method according to claim 1,
The at least one other user is a user who has a play history for at least one game among the plurality of recommended candidate games and a game in which the play history of the user exists.
The method according to claim 1,
The similarity is a Pearson correlation coefficient between the user's game-specific rating information and the one or more other users' game-specific rating information.
The method according to claim 1,
The step of calculating the expected rating,
Calculating a weighted sum of the rating information for each game of the one or more other users by using the similarity as a weight; And
And determining a value obtained by dividing the weighted sum by the sum of the similarities as the expected rating.
The method according to claim 1,
The method of recommending a game, further comprising providing game recommendation information including a name and an expected rating of the recommended game to the user.
A similarity calculation unit for calculating a similarity between the user and the one or more other users, respectively, based on the user's game-specific rating information and one or more other users' game-specific rating information;
An expected rating calculation unit that calculates an expected rating for each of a plurality of recommended candidate games based on the similarity; And
And a recommended game determination unit configured to determine a recommended candidate game having the highest predicted rating among the plurality of recommended candidate games as a recommended game to be recommended to the user.
The method of claim 8,
An information acquisition unit that acquires the user's preferred platform information and the user's preferred genre information; And
A game recommendation device further comprising a candidate determination unit configured to determine the plurality of recommended candidate games based on the preferred platform information and the preferred genre information.
The method of claim 9,
The plurality of recommendation candidate games includes a game serviced through a platform corresponding to the preferred platform information among a plurality of previously registered games and classified into a genre corresponding to the preferred genre information.
The method of claim 8,
The at least one other user is a user who has a play history for at least one game among the plurality of recommended candidate games and a game in which the play history of the user exists.
The method of claim 8,
The similarity is a Pearson correlation coefficient between the user's game-specific rating information and the one or more other users' game-specific rating information.
The method of claim 8,
The estimated rating calculation unit,
A weighted sum calculation unit for calculating a weighted sum of the rating information for each game of the one or more other users by using the similarity as a weight; And
And a predicted rating determining unit configured to determine a value obtained by dividing the weighted sum by the sum of the similarities as the predicted rating.
The method of claim 8,
Game recommendation device further comprising an information providing unit for providing the user with game recommendation information including the name and expected rating of the recommended game.

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