KR20200065426A - Platform system for recommendation of mobile game, method for recommending mobile game and computer program for the same - Google Patents

Abstract

A mobile game recommendation system includes: an information collecting unit configured to receive first type information including mobile game exposure history for a user, second type information including a mobile game browsing history of the user, and third type information including mobile game activity history of the user, from a mobile device of the user or a market server for providing a mobile game; an analysis unit configured to generate preference information of the user by analyzing at least one of the first type information, the second type information, and the third type information collected in the information collecting unit and generate analysis information on at least one target mobile game; and a matching unit configured to calculate a matching rate of the target mobile game for the user based on the preference information and the analysis information, and provide mobile game recommendation information, which is determined based on the matching rate, to the mobile device of the user. Accordingly, a game corresponding to the preference, which cannot be easily recognized by the user, is objectively recommended.

Description

Mobile game recommendation system, mobile game recommendation method and computer program therefor {PLATFORM SYSTEM FOR RECOMMENDATION OF MOBILE GAME, METHOD FOR RECOMMENDING MOBILE GAME AND COMPUTER PROGRAM FOR THE SAME}

Embodiments relate to a mobile game recommendation system, a mobile game recommendation method, and a computer program therefor, more specifically, a mobile game customized to an individual by quantifying personal preferences and game analysis information for selecting a user's game using a mobile game It is about a system that recommends and technology that uses this system as an advertising platform.

With the rapid development of technologies related to mobile communication devices such as smart phones, users' game activities are also shifting from the existing PC (Personal Computer) based to mobile devices such as smart phones.

Recently, several mobile games have been released a day, and information about games that can be accessed by general users is advertised information and the ranking and recommendations provided at the time of market visit until a separate time and effort is spent. The information selected on the ground is all. Even if a game that fits the user has been released, it is often impossible to find it from any of the above-mentioned paths, so it is often the case that only a few hundred of the released games are exposed and turn.

In addition, even if the user is interested and finds information about a mobile game that is suitable for him, there is a limitation that it is difficult to be sure which keyword to search for, what information to use, and which keyword to use finds the best result that the user wants to find. .

In this regard, various services such as Google Play Store, iOS App Store, and Community that provide mobile game-related content and/or markets provide game recommendation services in their respective forms, but until now, users are sympathetic and satisfactory. No service is provided. Therefore, to date, a large number of mobile game users often encounter the game through other limited and unobjective methods such as market popularity ranking, advertisement, and recommendation of friends.

Patent Publication No. 10-2006-0112723

According to one aspect of the present invention, a user can objectively define a user's game taste that is difficult to explain by himself, and can find and recommend a mobile game suitable therefor, and a recommendation method based on a conventional popular content recommendation method, category classification, or user group classification It is possible to provide a mobile game recommendation system and a method and a computer program for providing the recommended game information centered on the visitor's individual, beyond popular popularity or service provider-oriented sorting.

The mobile game recommendation system according to an exemplary embodiment includes first type information including a mobile game exposure history for a user and a user's mobile game viewing history from a user's mobile device or a market server providing a mobile game. An information collection unit configured to receive second type information including and third type information including a user's mobile game activity history; It is configured to generate user preference information by analyzing one or more of the first type information, the second type information, and the third type information collected in the information collection unit, and generate analysis information of one or more target mobile games. Analysis department; And a matching unit configured to calculate a matching rate of the target mobile game and a user based on the taste information and the analysis information, and provide mobile game recommendation information determined based on the matching rate to the mobile device of the user. Includes.

In one embodiment, the information collecting unit is further configured to receive the first type information and the second type information from the market server at least partially using an application programming interface (API) provided by the market server.

In one embodiment, the information collecting unit is further configured to receive the third type information from the mobile device.

In one embodiment, the first type information includes primary information including information about one or more of an inflow path of a game, a market type, a market ranking, an icon, a game name, a manufacturer's awareness, and a developing country; Secondary information, including information about one or more of the game's download count, recent update, release date, category, game propensity, popularity, screenshots, videos, and user ratings; And tertiary information including information related to game play.

In one embodiment, the second type information includes: click information including information on one or more of a page on which a click occurred, a menu on which a click occurred, a clicked content, a click related game, and a destination by a click; Search information including information about one or more of game name, game category, and game event type; And one or more of event participation information including information on one or more of the type of the participating event, the event-related game, and the issued coupon.

In one embodiment, the third type information includes: installation information including information about one or more of a game installation time and an installation path; And execution information including information on one or more of a first execution time of a game, a game play time per unit period, a number of game executions per unit period, a play start time, and a play end time.

In one embodiment, the analysis unit, based on one or more of the preference information determined based on one or more of the user's reaction information, interest information or installation information, and the user's play time, play count, play pattern and participation event coupon It is further configured to generate the taste information by using the loyalty information determined by and the departure information determined based on the cumulative play time of the user.

The mobile game recommendation system according to an embodiment further comprises an advertisement execution unit configured to generate optimal user information for an advertisement target game based on the taste information and the analysis information, and provide the optimal user information to an advertiser's user device Includes.

A mobile game recommendation method according to an embodiment of the present invention includes a first type of information including a mobile game exposure history for a user, a mobile game of a user, from a mobile game recommendation system, from a user's mobile device or a market server providing a mobile game Receiving second-class information including a browsing history and third-class information including a user's mobile game activity history; Generating, by the mobile game recommendation system, user preference information by analyzing one or more of the received first type information, the second type information, and the third type information; Generating, by the mobile game recommendation system, analysis information of one or more target mobile games; Calculating, by the mobile game recommendation system, a matching rate between the target mobile game and a user based on the taste information and the analysis information; And providing, by the mobile game recommendation system, the mobile game recommendation information determined based on the matching rate to the mobile device of the user.

In one embodiment, the receiving step, the mobile game recommendation system, the first type information and the second type information at least partially by using the market server provides an application programming interface (API) provided by the market And receiving from the server.

In one embodiment, the receiving step includes the mobile game recommendation system receiving the third type information from the mobile device.

In one embodiment, the generating of the preference information comprises: the preference information determined based on one or more of the user's reaction information, interest information, or installation information, the user's play time, and the number of plays And generating the preference information using loyalty information determined based on one or more of play patterns and participation event coupons, and departure information determined based on a user's cumulative play time.

A mobile game recommendation method according to an embodiment includes the steps of: generating, by the mobile game recommendation system, optimal user information for an advertisement target game based on the taste information and the analysis information; And providing, by the mobile game recommendation system, the optimal user information to an advertiser's user device.

The computer program according to an embodiment may be stored in a computer-readable recording medium as a method for executing a mobile game recommendation method according to the above-described embodiments in combination with hardware.

Using the mobile game recommendation system and method according to an aspect of the present invention, a user can objectively define a game taste of a user who is difficult to describe himself, and find and recommend a mobile game suitable therefor, and a conventional popular content recommendation method or category It is possible to provide recommended game information centered on a visitor's individual, beyond popular popularity or service provider-oriented sorting, such as a recommendation method based on classification or user group classification.

The mobile game recommendation system and method according to an aspect of the present invention is performed in parallel with the operation of a service providing game information such as game news delivery, event participation, user community, game recommendation, market ranking, and so on, and analyzes the game and game market information. , Collect user play records, etc., analyze user's play experience, play pattern, and response to content to identify user's preferences, loyalty, and satisfaction for games, and through this, predict preferences, expected loyalty of games that have not yet been experienced , You can predict the expected satisfaction. Through the above prediction results, personalized game information can be provided to users.

In addition, the mobile game recommendation system and method according to an aspect of the present invention may perform a function of an advertising platform based on a system for recommending a customized mobile game to an individual user. Through this, it is possible to provide an optimal user information suitable for a game at a low cost to an advertiser (for example, a game company) requesting an advertisement. As a result, advertisers can prepare a guide for material creation through accurate target user prediction and user preference information before marketing, and prepare various materials and show each user's favorite material to the user. Since it is possible, it is possible to increase the inflow rate of the user to the mobile game.

Furthermore, using the mobile game recommendation system and method according to an aspect of the present invention, the mobile game is introduced to users based on the user's taste and the content of the game, beyond the latest or popular ranking of the market that has been used in the past, Mobile game makers can be given the opportunity to introduce more games to users in addition to the way through marketing, widening the choice of mobile games to users, and allowing users to easily find games that suit their tastes. By doing so, you can make the game more active.

1 is a schematic block diagram of a mobile game recommendation system according to an embodiment.
2 is a flowchart of a mobile game recommendation method according to an embodiment.
3 is a flow chart of a mobile game recommendation method according to another embodiment.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

1 is a schematic block diagram of a mobile game recommendation system according to an embodiment. The mobile game recommendation system 3 according to embodiments communicates with the user's mobile device 1, the advertiser's user device 4 and one or more external servers 2 and 5 via a wired and/or wireless network. can do. The communication method through the wired and/or wireless network may include an object and all communication methods capable of networking the object, and is not limited to wired communication, wireless communication, 3G, 4G, or other methods.

For example, wired and/or wireless networks include Local Area Network (LAN), Metropolitan Area Network (MAN), Global System for Mobile Network (GSM), Enhanced Data GSM Environment (EDGE), and High Speed Downlink Packet Access (HSDPA). , Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth, Zigbee, Wi-Fi, VoIP (Voice) over Internet Protocol), LTE Advanced, IEEE802.16m, WirelessMAN-Advanced, HSPA+, 3GPP Long Term Evolution (LTE), Mobile WiMAX (IEEE 802.16e), UMB (formerly EV-DO Rev. C), Flash-OFDM, iBurst and MBWA (IEEE 802.20) systems, HIPERMAN, Beam-Division Multiple Access (BDMA), Wi-MAX (World Interoperability for Microwave Access), and refers to a communication network by one or more communication methods selected from the group consisting of ultrasonic communication. However, it is not limited thereto.

The mobile device 1 is a device used by a user playing a mobile game. In FIG. 1, the mobile device 1 is shown in the form of a smart phone, but the mobile device 1 is a mobile communication device that can be carried by a user, such as a mobile communication terminal of another type, a tablet computer, or a portable game machine. It may be any type of device that can be executed.

In addition, the user device 4 is a device used by an advertiser who wants to market using the mobile game recommendation system 3 as an advertising platform. In FIG. 1, the user device 4 is shown in the form of a notebook computer, but is not limited thereto, and the user device 4 is a mobile communication terminal, a personal computer, a personal digital assistant (PDA), a tablet (tablet). ) It may be implemented in the form of an arbitrary computing device such as a set-top box for a computer, an IPTV (Internet Protocol Television), or a server accessible from another device.

The number of each of the mobile device 1 and the user device 4 shown in FIG. 1 is merely illustrative for convenience and does not indicate the number of users or advertisers using the mobile game recommendation system 3. Will be understood.

The user of the mobile game can access the market server 2 through the wired and/or wireless network from the mobile device 1 and play the mobile game provided by the market server. The market server 2 is a service provider's server that provides mobile content, and may refer to a market server such as a Google Play Store or an iOS App Store, but is not limited thereto.

The mobile game recommendation system 3 according to the embodiments may receive at least a part of information necessary for recommending a game to a user from the market server 2. In addition, the mobile game recommendation system 3 may receive another part of the information necessary for recommending the game to the user from the user's mobile device 1.

In one embodiment, the mobile game recommendation system 3 may receive some of the information necessary to recommend the game to the user from one or more external servers 5 other than the market server 2. At this time, the external server 5 may be a server of a game maker that provides information on a specific mobile game, or a game-related community server in which information about a plurality of mobile games is uploaded by a service operator or users. It may be.

In one embodiment, the mobile game recommendation system 3 includes an information collection unit 31, an analysis unit 33, and a matching unit 34. In one embodiment, the mobile game recommendation system 3 further includes a database (DB) 32. In addition, in one embodiment, the mobile game recommendation system 3 further includes an advertisement executive unit 35.

The mobile game recommendation system 3 according to the embodiments may have aspects that are entirely hardware, or partly hardware and partly software. For example, each unit included in the mobile game recommendation system 3 may collectively refer to hardware and software related to processing data of a specific format and content and/or electronic communication. The terms "part", "module", "device", "terminal", "server" or "system" in this specification are intended to refer to a combination of hardware and software driven by the hardware. For example, the hardware may be a data processing device that includes a CPU or other processor. Also, software driven by hardware may refer to a running process, an object, an executable, a thread of execution, a program, or the like.

Further, each element constituting the mobile game recommendation system 3 according to the present embodiments is not intended to refer to separate devices that are physically separated from each other. That is, the information collection unit 31, DB 32, analysis unit 33, matching unit 35, and advertisement execution unit 35 of FIG. 1 provide hardware constituting the mobile game recommendation system 3 to the corresponding hardware. It is only functionally divided according to the operation performed by each, and each part is not necessarily provided independently of each other. Of course, depending on the embodiment, one or more of the information collection unit 31, the DB 32, the analysis unit 33, the matching unit 35, and the advertisement execution unit 35 are implemented as separate devices that are physically separated from each other. It is also possible.

In the present specification, a mobile game refers to any content that a user can play on the mobile device 1 by downloading to the mobile device 1 or executing a program pre-stored in the mobile device 1, and a specific category and format. Or a game with content. For example, the mobile game of the present specification may be executed alone by the mobile device 1, or may involve communication with a game server (not shown) or another user's mobile device in the execution process. .

The information collecting unit 31 collects information, which is the basis of the analysis required to recommend the mobile game to the user, from the user's mobile device 1 or the market server 2 that provides the mobile game. In addition, the information collection unit 31 can also collect information from one or more other external servers 5. The information collected by the information collection unit 31 includes first type information including a mobile game exposure history for the user, second type information including a user's mobile game viewing history, and a user's mobile game activity history. It may include third-class information.

The information collecting unit 31 may receive the first type information and/or the second type information using an application programming interface (API) provided by the market server 2 or another external server. In addition, the information collection unit 31 may collect the third type of information from the mobile device 1 in a manner such as crawling on the user's mobile device 1.

In the present specification, the first type information includes primary information about a game inflow path, market type, market ranking, icon, game name, manufacturer recognition, and development country; Secondary information about the number of games downloaded, recent updates, release date, category, game propensity, popularity, screenshots, videos and user ratings; And/or tertiary information related to game play.

In addition, the second kind of information includes click information on a page on which a click occurred, a menu on which a click occurred, clicked content, a click-related game, and a destination by click; Search information for game names, game categories, and game event types; And/or event participation information for the type of the participating event, the event-related game, and the issued coupon.

In addition, the third kind of information includes installation information on the installation time and installation path of the game; And/or execution information for the first execution time of the game, game play time for each unit period, number of game executions for each unit period, play start time, and play end time.

The analysis unit 33 may generate user preference information by analyzing one or more of the first type information, the second type information, and the third type information collected in the information collection unit 31. Also, the analysis unit 33 may generate analysis information of one or more target mobile games. User preference information and game analysis information generated by the analysis unit 33 may be stored in the DB 32. For example, the analysis unit 33 may include preference information determined based on one or more of the user's reaction information, interest information, or installation information, and one or more of the user's play time, play count, play pattern, and participation event coupon. The preference information of the user may be generated using loyalty information determined based on the information and departure information determined based on the cumulative play time of the user. The specific form and analysis method of each information will be described later in detail with reference to FIG. 2.

The matching unit 34 calculates a matching rate between a specific mobile game and a user based on user preference information and game analysis information analyzed by the analysis unit 33, and recommends it to the user based on the matching rate Recommendation information for mobile games may be determined. Further, the matching unit 34 may transmit the determined mobile game recommendation information to the user's mobile device 1. A specific method of calculating the matching rate will be described later with reference to FIG. 2.

The advertisement execution unit 35 derives the optimal user or user group for the advertisement target game based on the user's taste information generated by the analysis unit 33 and the analysis information for the game, and obtains optimal user information including the same. It can be provided to the advertiser through the user device (4). In addition, the advertisement executive unit 35 may receive an advertisement execution request based on optimal user information from the advertiser's user device 4 and provide advertisements to the mobile device 1 of one or more users corresponding to the optimal user. . For example, the advertisement may be provided as part of mobile game recommendation information provided to the mobile device 1.

2 is a flowchart of a mobile game recommendation method according to an embodiment. For convenience of description, a mobile game recommendation method according to this embodiment will be described with reference to FIGS. 1 and 2.

First, the user can access the market server 2 and browse the mobile game provided by the market server 2 (S1). The reading of the mobile game is generally performed on the mobile device 1 that will actually play the mobile game, but is not limited to this, and the user can play the game provided by the market server 2 using another computing device such as the user's PC. You can read it.

At this time, the market server 2 may provide the mobile game recommendation system 3 with the segment value of each user who views the contents of the market server 2 and the user's activity information (S2). For example, the Google Play Store provides basic information on mobile games, popularity rankings, and user browsing history through an API (Application Programming Interface), so the information collection unit 31 of the mobile game recommendation system 3 ) May collect necessary information from the market server 2 using these APIs.

For example, the information collected by the information collection unit 31 from the market server 2 includes first type information corresponding to data for game analysis, and second type information corresponding to user activity on the market server 2. It may include. Also, in one embodiment, the first type information may be classified as primary to tertiary information.

The primary information includes information such as the inflow path of the game, market type, market ranking, icon, game name, manufacturer awareness, and developing country. At this time, the primary information is processed qualitatively by the operator of the mobile game recommendation system (3) or the analyst agreed with the mobile game recommendation system (3) for the category propensity, target target, figure, main content, characters, and platforms. It can be supplemented.

In addition, the secondary information includes information on the number of downloads of games, recent updates, release dates, categories, game propensity, popularity, screenshots, videos, and user ratings. At this time, the game propensity information may be used in the form provided by the market server 2, or the game goal, play emphasis, opponents, whether to play automatically, win condition, play type, multiple access personnel, presence of community, etc. Based on the may be qualitatively processed or supplemented by the operator of the mobile game recommendation system 3 or an analyst contracted with the mobile game recommendation system 3. Similarly, screenshots or video information may be qualitatively processed or supplemented based on pictures, characters, viewpoints, background eras, and genres. In addition, the popularity may use any one of the highest popularity ranking, the current popularity ranking, and the current sales ranking provided by the market server 2, or may use it comprehensively.

In addition, the tertiary information is information related to game play, and may include information such as a game installation path, completeness, content quality, story dependency, user interface evaluation, and the like provided by the market server 2 May be used, or may be qualitatively processed or supplemented by an operator of the mobile game recommendation system 3 or an analyst contracted with the mobile game recommendation system 3.

In addition, the information collected by the mobile game recommendation system 3 from the market server 2 may include second type information corresponding to user activity on the market server 2. In this case, the second kind of information includes click information such as a page where a click occurred, a menu where a click occurred, clicked content, click-related game, click destination, and search information for a game name, game category, and game event type, It can be divided into event participation information on the type of the participating event, the event-related game, and the issued coupon. That is, the second type information may include one or more of click information, search information, and event participation.

As a result of viewing the market server 2, the user can download a specific mobile game to be played on the mobile device 1 (S3). In addition, the user can play the downloaded game on the mobile device 1 (S4). At this time, the mobile device 1 may collect information such as a game installed by a user, a game installation path, a game play time, and a number of uses. Such information may be collected using a function provided by the operating system of the mobile device 1, or an application (or, separately installed in the mobile device 1) to use a service provided by the mobile game recommendation system 3 It may be collected by crawling through an app).

The information collecting unit 31 of the mobile game recommendation system 3 may receive the information collected about the user's game play from the mobile device 1 as third type information (S5). For example, the third kind of information is information about the installation such as the installation time and installation path of the game (installed app or page, etc.), the first execution time of the game, the game play time by unit period (for example, daily), and by unit period (for example, , Daily) It may include information about the execution, such as the number of game executions, the play start time and the play end time. The collected third type information may be periodically (eg, once a day) transmitted from the mobile device 1 to the mobile game recommendation system 3.

Next, the analysis unit 33 of the mobile game recommendation system 3 analyzes the collected first to third types of information to analyze the target games, that is, the mobile games provided by the market server 2. It also generates taste information for the mobile game of the user of the mobile device 1 (S6). For example, the analysis information of the target game may be generated based on the first type information, and the user's preference information may be generated based on the second type information and the third type information. In addition, although not shown in the figure, the mobile game recommendation system 3 directly receives at least a part of the first type information and/or the second type information from an advertiser, such as a game company, advertising agency, media lab company, etc. It may be collected through an external server 5 such as a company server, a community server, or a media company server.

First, analysis of the target game may be performed based on the first type information. For example, an analysis table capable of classifying various characteristics of a game is established based on a decision made by an analysis member or a group of analysis members, and different analysis members can obtain the same analysis result in the same game based on the analysis table. So that it can be quantified. At this time, the analysis unit 33 receives analysis results from a user device (not shown) of one or more analysis members based on an analysis table set in advance by the above process, and based on this, generates analysis information of a target mobile game. Can be.

Next, the user's taste information analysis may be performed as follows based on the second type information and the third type information.

0. First, the user's activity pattern is derived from the market server 2 through a page frequently visited by a user, an installation path of a game, a moving line, a usage environment, and participating event information, and the user's activity pattern, that is, the user Figure out what items you are interested in.

1. Also, from the information collected through the user's activities, grasp the characteristics of the user's reaction at the first contact point between the game and the user through the image, video material, color, and mood. Since the preference of users at the first point of contact is the most important factor in marketing, it can be used to evaluate in advance whether the target users' propensity is well reflected in the advertisement.

2. In addition, it quantifies social evaluations such as popularity, issues, expertise, public opinion, and game management awareness of mobile games, and popular games based on the market rank of games, search queries on search portals, and number of cafe users. Evaluate perception. Next, through the user's participation information for each mobile game in which social evaluation is quantified, the degree to which the social evaluation result affects the user's game selection and the effect on the user's loyalty are analyzed.

3. In addition, it is possible to analyze the user's propensity for new games. Users who are interested in the ranker competition that can exert influence within the game want to start the game faster than anyone else in order to preempt the advantage ahead of others, and these users are likely to choose the most recent release under similar conditions. If the entry to the game launch fails, it is often done by selecting a new server, so it is intended to reflect this tendency of users in taste analysis.

4. In addition, it is possible to analyze the user's taste based on the category of the mobile game. The category reflects representative characteristics of games that can be sympathized by the public, so that the user can grasp a wide range of game information that interests the user. Since various category elements are compounded in many games, it is possible to apply multiple categories for each game and to widen the content of the user's interest through it, or to narrow the scope to a game that contains only overlapping information and to identify the main interests. .

5. In addition, the characteristics of the content, such as the characteristics of the characters appearing in the game (zombies, animals, dinosaurs, robots, etc.), popular intellectual property (IP), materials (pictures, music, historical background, myths, etc.) User preferences can be analyzed.

6. In addition, the user's preference for this can be analyzed by identifying the role and participation method of the user in the mobile game, such as the progress, operation method, and play goal of the mobile game. This is the part that the user plays while playing the actual game, which is the most important reason not to choose or select the game, and has the most influence on loyalty, and is an important factor in which the taste of each user is most clearly changed.

Finally, the user's personal priorities of the items described in the above 0 to 6 are grasped and the user's preference information can be determined. Each item of 0 to 6 may be quantified as a final analysis result by applying different weights according to a user's preference.

In one embodiment, the user preference information generated by the above process is preference information determined based on one or more of the user's reaction information, interest information, or installation information, and the user's play time, play count, play pattern, and participation event It is generated using loyalty information determined based on one or more of the coupons and departure information determined based on a user's cumulative play time.

At this time, the response information includes information on a menu in which a user frequently clicks, a page in which a user clicks frequently, and a market section in which a user clicks in the second kind of information, and an icon, banner of a material that the user reacts to by clicking, etc. , It may include material information such as thumbnails, game titles, categories, and IP. In addition, in the second class information, interest information includes additional activities such as user comments, individual game schedule view, screenshot view, game details introduction, video view, user's event participation, search for keywords, categories, events, etc. And clicks. Furthermore, the installation information may include information on a path where a user has downloaded a game from the market server 2.

In addition, the loyalty information may include information after execution such as a user's unit period (eg, daily) play time and number of plays, play pattern (eg, time zone or day of play), participation event coupon confirmation, and the like. In one embodiment, satisfaction may be derived by collecting or adding user preference information and loyalty information.

In addition, the exit information refers to the user's play stop time or cumulative play time in the third type information, for example, if the user deviates within 15 minutes after running the mobile game, or deviates within 30 minutes, deviates within 1 hour. The user's preferences can be defined by dividing them into the case, and when leaving within 6 to 24 hours.

In one embodiment, the analysis unit 33 may store the user preference information generated as described above and the analysis information of the mobile game in the DB 32 of the mobile game recommendation system 3.

Next, the matching unit 34 of the mobile game recommendation system 3 based on the user's taste information generated by the analysis unit 33 and the analysis information for the game, matches the matching rate between each mobile game and the user. It can be calculated (S7). Also, the matching unit 34 may transmit the mobile game recommendation information determined based on the matching rate to the mobile device 1 of the user (S8). For example, the mobile game recommendation information may consist of information about the mobile game in which the matching rate with the user is greater than or equal to a predetermined threshold.

Specifically, the matching rate between the game to be analyzed and the user can be calculated based on the average value for each analysis item of each game by selecting the game to be analyzed according to the purpose of obtaining the number.

For example, in order to obtain game information having a high match rate based on a user's play experience, games having a play history of a certain time (eg, 100 hours) or more for a certain period (eg, the last 2 years) may be selected as a target. Comprehensive matching rate calculation for generating mobile game recommendation information, game selection and play pattern of each user, such as whether the user is interested in recent interests, ranking and popularity, recently released sensitivity, brand preference, etc., identified through the user's taste information It can be made in such a way that weights are given for each item. In this case, depending on which characteristic of the game having the user's play experience occupies a high weight, the target game having the characteristic may be given a high weight.

The weight can be determined, for example, as follows.

-When including recent (eg, within 5) search terms: +10

-For games with the highest popularity ranking (e.g., in the top 10): + (percentage of games that have been ranked in popularity among users who have a user's play experience × 10)

-In the case of recent releases (e.g., within 2 weeks): + (the percentage of games with user play experience that started within the relevant period after release (i.e., 2 weeks, which is the basis of the latest release) × 10)

-When the history of playing the game of the same publisher is less than a threshold (for example, 10 times): + (the number of games played by the same publisher)

-When the history of playing the game of the same publisher exceeds the threshold (eg, 10 times): +10

The respective values of the weights described above and reference values for calculating them are exemplary and may be variously set according to embodiments.

In addition, the whole calculation process of the matching rate is as follows.

1. Matching purpose selection (receiving user input)

2. Game selection for calculation by matching purpose

3. Derivation of average value for each analysis item of the game to be calculated

-For numerical notation (eg 0 to 100), use the average value

-Selection (eg, O, X) items are calculated according to the selection ratio

Ex) (Number of games marked with O) / (Number of games counted) × 100

4. Preliminary selection of top items (eg, top 30%) based on the average value of each item obtained

5. Determine the target game within the allowable range (eg +-15%) based on the average value of the preselected items

6. Calculate the matching rate for each analyzed item of the determined target games

-For numeric notation (eg 0 to 100): [100-| (Average value for each analysis item)-(Average value for the analysis item of the target game within the allowable range) |]

-In case of selection (eg, O, X): In case of O, average value of each analysis item / In case of X [100-(average value of each analysis item)]

7. Calculation of match rate of each game: average value of calculated values for each item

8. Calculate weight items

9. Final summation: Matching rate + weighted sum of each game

However, the method of calculating the matching rate described above is exemplary, and the matching rate may be calculated based on different information from those described above depending on the embodiment and its purpose, and/or may be calculated by different formulas from those described above. have.

3 is a flow chart of a mobile game recommendation method according to another embodiment. The embodiment illustrated in FIG. 3 shows an operation when the mobile game recommendation system according to the present invention is used as an advertising platform.

1 and 3, the advertiser may request the mobile game recommendation system 3 to analyze the optimal user for the mobile game targeted for advertisement by using the user device 4 (S11).

The advertisement executive unit 35 of the mobile game recommendation system 3, in response to the request received from the advertiser, receives first to third type information through communication with the mobile device 1 (S12), and receives Analysis of the advertisement target game and user preference information may be generated through analysis using the information (S13). In the drawing, for convenience of description, an example in which the first type to third type information is received from the mobile device 1 is shown, but at least some of the first type to third type information is transmitted from the mobile device 1 to the market server ( It may be received via 2) or only from the market server 2 or may be received from one or more external servers 5.

The process of analyzing the advertisement target game and the process of analyzing the user's taste may be performed by the analysis unit 33, wherein the user's taste analysis is performed for each of a plurality of users using the mobile game recommendation system 3 do. The analysis process is the same as the corresponding steps in the above-described embodiment with reference to FIG. 2, so a detailed description is omitted to avoid duplication of the description.

Next, the matching unit 34 may derive optimal user information consisting of an optimal user or users matching the advertisement target game based on the analysis information on the advertisement target game and the user's preference information (S14). . The advertisement execution unit 35 may transmit the optimal user information derived as described above to the advertiser's user device 4 (S15). Through this, the advertiser can acquire high-quality user information that is well suited to the mobile game he or she wants to market.

In one embodiment, when the advertiser requests a customized advertisement from the mobile game recommendation system 3 based on the optimal user information provided from the mobile game recommendation system 3 (S16), the advertisement execution unit 35 provides the optimal user The advertisement for the advertisement target game may be transmitted to the corresponding one or more users (S17). The advertisement may be transmitted as part of the game recommendation information provided to the user from the mobile game recommendation system 3, for example, a part of the recommended game list may include an advertisement target game or an advertisement target game on a presentation screen of the recommended game list A banner advertisement may be displayed. However, this is exemplary, and in another embodiment, the advertisement may be transmitted to the mobile device 1 separately from the game recommendation information.

The operation by the mobile game recommendation method according to the above-described embodiments may be at least partially implemented as a computer program and recorded in a computer-readable recording medium. A program for implementing an operation by a mobile game recommendation method according to embodiments is recorded, and a computer-readable recording medium includes all types of recording devices in which data that can be read by a computer are stored. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disks, and optical data storage devices. In addition, the computer readable recording medium may be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing the present embodiment will be readily understood by those skilled in the art to which this embodiment belongs.

The present invention described above has been described with reference to the embodiments shown in the drawings, but this is merely exemplary, and those skilled in the art will understand that various modifications and modifications of the embodiments are possible therefrom. However, it should be considered that such modifications are within the technical protection scope of the present invention. Therefore, the true technical protection scope of the present invention should be defined by the technical spirit of the appended claims.

Claims (17)

From a market server providing a user's mobile device or mobile game, first type information including a mobile game exposure history for the user, second type information including a user's mobile game viewing history, and a user's mobile game activity history An information collecting unit configured to receive third type information including;
It is configured to generate user preference information by analyzing one or more of the first type information, the second type information, and the third type information collected in the information collection unit and generate analysis information of one or more target mobile games. Analysis department; And
A mobile game including a matching unit configured to calculate a matching rate between the target mobile game and a user based on the taste information and the analysis information, and provide mobile game recommendation information determined based on the matching rate to the mobile device of the user Recommendation system. According to claim 1,
The information collecting unit is a mobile game recommendation system further configured to receive the first type information and the second type information from the market server at least partially using an application programming interface (API) provided by the market server. The method according to claim 1 or 2,
The information collecting unit is a mobile game recommendation system further configured to receive the third type of information from the mobile device. According to claim 1,
The first type information,
Primary information, including information about one or more of the funnel, market type, market ranking, icon, game name, publisher awareness, and developing country;
Secondary information, including information about one or more of the game's download count, recent update, release date, category, game propensity, popularity, screenshots, videos, and user ratings; And
A mobile game recommendation system that includes one or more of tertiary information including information related to game play. According to claim 1,
The second type information,
Click information including information about one or more of a page on which a click occurred, a menu on which a click occurred, clicked content, click-related games, and a destination of a click;
Search information including information about one or more of game name, game category, and game event type; And
A mobile game recommendation system including one or more of event participation information including information on one or more of a participating event type, an event-related game, and a issued coupon. According to claim 1,
The third kind of information,
Installation information including information about one or more of the installation time and installation path of the game; And
A mobile game recommendation system including one or more of execution information including information on one or more of a first execution time of a game, game play time per unit period, number of game executions per unit period, play start time, and play end time. According to claim 1,
The analysis unit may include preference information determined based on one or more of the user's reaction information, interest information, or installation information, and loyalty information determined based on one or more of a user's play time, play count, play pattern, and participation event coupon And, a mobile game recommendation system further configured to generate the taste information using departure information determined based on a user's cumulative play time. According to claim 1,
A mobile game recommendation system further comprising an advertisement executive unit configured to generate optimal user information for an advertisement target game based on the taste information and the analysis information, and provide the optimal user information to an advertiser's user device. A first type information including a mobile game exposure history for a user, a second type information including a user's mobile game viewing history, and a mobile game recommendation system from a market server providing a user's mobile device or a mobile game, and Receiving type 3 information including a user's mobile game activity history;
Generating, by the mobile game recommendation system, user preference information by analyzing one or more of the received first type information, the second type information, and the third type information;
Generating, by the mobile game recommendation system, analysis information of one or more target mobile games;
Calculating, by the mobile game recommendation system, a matching rate between the target mobile game and a user based on the taste information and the analysis information; And
And providing, by the mobile game recommendation system, the mobile game recommendation information determined based on the matching rate to the mobile device of the user. The method of claim 9,
The receiving may include receiving, by the mobile game recommendation system, the first type information and the second type information from the market server at least partially using an application programming interface (API) provided by the market server. Mobile game recommendation method comprising a. The method of claim 9 or 10,
The receiving step, the mobile game recommendation system, the mobile game recommendation method comprising the step of receiving the third type information from the mobile device. The method of claim 9,
The first type information,
Primary information, including information about one or more of the funnel, market type, market ranking, icon, game name, publisher awareness, and developing country;
Secondary information, including information about one or more of the number of downloads, recent updates, release date, category, game propensity, popularity, screenshots, videos, and user ratings of the game; And
A mobile game recommendation method comprising one or more of tertiary information including information related to game play. The method of claim 9,
The second type information,
Click information including information about one or more of a page on which a click occurred, a menu on which a click occurred, clicked content, click-related games, and a destination of a click;
Search information including information about one or more of game name, game category, and game event type; And
A mobile game recommendation method that includes one or more of event participation information, including information about one or more of the participating event type, the event-related game, and the issued coupon. The method of claim 9,
The third kind of information,
Installation information including information about one or more of the installation time and installation path of the game; And
A method for recommending a mobile game including at least one of execution information including information on one or more of a first execution time of a game, game play time per unit period, number of game executions per unit period, play start time, and play end time. The method of claim 9,
In the generating of the preference information, the mobile game recommendation system, preference information determined based on one or more of the user's reaction information, interest information, or installation information, and the user's play time, play count, play pattern and participation And generating the taste information using loyalty information determined based on one or more of the event coupons and departure information determined based on a user's cumulative play time. The method of claim 9,
Generating, by the mobile game recommendation system, optimal user information for an advertisement target game based on the taste information and the analysis information; And
And the mobile game recommendation system further comprising providing the optimal user information to an advertiser's user device. A computer program stored in a computer-readable recording medium to execute the mobile game recommendation method according to any one of claims 9 to 16 in combination with hardware.

Images