Game room matching method, device, readable medium and electronic device

Abstract

The present disclosure relates to a game room matching method, apparatus, readable medium, and electronic device. The game room matching method receives a game room joining request from a target user and obtains user feature information of the target user carried in the request; obtains a plurality of rooms to be matched and room feature information of each of the rooms to be matched; determines a plurality of preset metrics of the target user in each of the rooms to be matched according to the user feature information and the room feature information of each of the rooms to be matched; sorts the plurality of rooms to be matched according to the plurality of preset metrics corresponding to each of the rooms to be matched to determine at least one target matching room, which can provide a game room with a higher matching degree for the target user through a plurality of preset metrics, thereby effectively improving the gaming experience of game player users.

Description

Technical Field

[0001] The present disclosure relates to the field of online games, and in particular, to a game room matching method, device, readable medium, and electronic device. Background Art

[0002] In board games, when a player user needs to join a game room to play, generally, the game room matching is performed according to the remaining empty seats in the game room, that is, the player user is recommended to a game room that is about to be full, so as to ensure that the game can start as soon as possible. However, the inventor has found that the current game room matching method often causes the player user to frequently change the game room, have arguments with other player users in the game room, or be kicked out of the room by other player users, etc., which are unpleasant game experiences, that is, it is easy to cause the problem of unsmooth user game experience. Summary of the Invention

[0003] This summary of the invention is provided to introduce concepts in a brief form that will be described in detail in the following detailed implementation section. This summary of the invention is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to be used to limit the scope of the claimed technical solution.

[0004] The purpose of the present disclosure is to provide a game room matching method, device, readable medium, and electronic device.

[0005] In a first aspect, the present disclosure provides a game room matching method, and the method includes:

[0006] Receiving a game room joining request of a target user, and obtaining user feature information of the target user carried in the request;

[0007] Obtaining a plurality of rooms to be matched, and room feature information of each of the rooms to be matched;

[0008] Determining a plurality of preset metrics of the target user in each of the rooms to be matched according to the user feature information and the room feature information of each of the rooms to be matched;

[0009] Sorting the plurality of rooms to be matched according to the plurality of preset metrics corresponding to each of the rooms to be matched to determine at least one target matching room.

[0010] In a second aspect, the present disclosure provides a game room matching device, and the device includes:

[0011] A first obtaining module, configured to receive a game room joining request of a target user, and obtain user feature information of the target user carried in the request;

[0012] A second acquisition module, configured to acquire a plurality of rooms to be matched with available spaces, and room feature information of each of the rooms to be matched;

[0013] A first determination module, configured to determine a plurality of preset metrics of the target user in each of the rooms to be matched according to the user feature information and the room feature information of each of the rooms to be matched;

[0014] A second determination module, configured to sort the plurality of rooms to be matched according to the plurality of preset metrics corresponding to each of the rooms to be matched, so as to determine at least one target matching room.

[0015] In a third aspect, the present disclosure provides a computer-readable medium, on which a computer program is stored, and when the program is executed by a processing device, the steps of the method described in the above first aspect are implemented.

[0016] In a fourth aspect, the present disclosure provides an electronic device, including:

[0017] A storage device, on which a computer program is stored;

[0018] A processing device, configured to execute the computer program in the storage device to implement the steps of the method described in the above first aspect.

[0019] In the above technical solution, by determining a plurality of preset metrics of the target user in each of the rooms to be matched according to the user feature information and the room feature information of each of the rooms to be matched; sorting the plurality of rooms to be matched according to the plurality of preset metrics corresponding to each of the rooms to be matched, so as to determine at least one target matching room, it is possible to provide a game room with a higher matching degree for the target user through a plurality of preset metrics, thereby effectively improving the game experience of game player users.

[0020] Other features and advantages of the present disclosure will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Combined with the drawings and referring to the following specific implementation manners, the above and other features, advantages and aspects of the embodiments of the present disclosure will become more obvious. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic, and the original elements and elements are not necessarily drawn to scale. In the drawings:

[0022] Figure 1 is a flowchart of a game room matching method shown in an exemplary embodiment of the present disclosure;

[0023] Figure 2 is a schematic diagram of a game room matching process shown in an exemplary embodiment of the present disclosure;

[0024] Figure 3 It is a schematic diagram of an algorithm matching framework shown in an exemplary embodiment of the present disclosure;

[0025] Figure 4 It is according to the present disclosure Figure 1 The flowchart of a game room matching method shown in the illustrated embodiment;

[0026] Figure 5 It is the flowchart of a model training method shown in an exemplary embodiment of the present disclosure;

[0027] Figure 6 It is the block diagram of a game room matching device shown in an exemplary embodiment of the present disclosure;

[0028] Figure 7 It is according to the present disclosure Figure 6 The block diagram of a game room matching device shown in the illustrated embodiment;

[0029] Figure 8 It is the block diagram of an electronic device shown in an exemplary embodiment of the present disclosure. Detailed implementation manners

[0030] Hereinafter, embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0031] It should be understood that the various steps recorded in the method embodiments of the present disclosure can be executed in different orders and / or executed in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this regard.

[0032] The term "including" and its variations used herein are open-ended, that is, "including but not limited to". The term "based on" is "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0033] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order of the functions executed by these devices, modules or units or their interdependent relationships.

[0034] It should be noted that the modifications of "one" and "multiple" mentioned in this disclosure are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise clearly specified in the context, it should be understood as "one or more".

[0035] The names of the messages or information exchanged between multiple devices in the embodiments of this disclosure are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0036] Before introducing the specific embodiments of this disclosure in detail, the application scenarios of this disclosure are described as follows. This disclosure can be applied to the process of matching game rooms for player users in a game. In the current game room matching methods in related technologies, usually player users are matched to game rooms that are about to be full, so as to ensure that the game can start as soon as possible, in order to improve the game experience of player users, or player users with similar win rates are matched to a game room, so as to try to match player users to rooms with comparable strength and about to be full, thereby further enhancing the game experience of player users. However, the inventor has found that due to the fact that the game experience is affected by multiple factors, even when the win rates of multiple game players in the same game room are close, there are still problems with the smoothness of the user game experience.

[0037] To solve the above technical problems, this disclosure provides a game room matching method, device, readable medium and electronic device. This game room matching method receives a game room joining request of a target user and obtains the user characteristic information of the target user carried in the request; obtains multiple rooms to be matched and the room characteristic information of each of the rooms to be matched; determines multiple preset metrics of the target user in each of the rooms to be matched according to the user characteristic information and the room characteristic information of each of the rooms to be matched; sorts the multiple rooms to be matched according to the multiple preset metrics corresponding to each of the rooms to be matched to determine at least one target matching room, which can provide a game room with a higher matching degree for the target user through multiple preset metrics, thereby effectively improving the game experience of game player users.

[0038] The technical solutions of this disclosure are elaborated in detail below in conjunction with specific embodiments.

[0039] Figure 1 It is a flowchart of a game room matching method shown in an exemplary embodiment of this disclosure; as Figure 1 shown, the method may include the following steps:

[0040] Step 101, receive a game room joining request of a target user and obtain the user characteristic information of the target user carried in the request.

[0041] Among them, the user feature information includes at least one of identity features, game attribute features, room attribute features, and social attribute features authorized by the user. It should be noted that all information in this solution is authorized by the user. When the matching is initiated, a prompt for whether to authorize the unauthorized information will be sent to the user. If the authorization is not obtained, the matching will be stopped or only the information that has been authorized will be used for matching. The identity features may include identification information such as user ID, avatar, name, etc.; the game attribute features may include attribute features related to in-game events such as game level, recent win rate, game duration, etc.; the room attribute features may include the maximum number of people, the number of robots, room level, room type, etc. in the game room where the user is located within a historical time period, which are related to the room where the user completes the game; the social attribute features may include attribute features related to public social behaviors such as speaking, voting, and liking during the game process of the user within a historical time period.

[0042] Step 102, obtain a plurality of rooms to be matched, and the room feature information of each of the rooms to be matched.

[0043] In this step, the game rooms with vacancies can be determined as the rooms to be matched. The room feature information may include at least one of room configuration features, room user attribute features, room game attribute features, and room style features; among them, the room style features may include one or more of the frequency of adding new users, user change frequency, and the proportion of users waiting for the next round to start within a historical time period.

[0044] It should be noted that the room configuration features may include identification information such as game room ID, image, name, etc., the maximum number of people that the room can accommodate, room level, room type; the user attribute features may include information such as the number of users configured in the room, the number of user robots, etc. The room game attribute features may include the room existence duration, the number of games played in the room, the recent win rate of the room users, the room user level, and the comprehensive features of other users in the room. The comprehensive features may include all or part of the user feature information of each of the other users in the current room. The user feature information may be the user feature information shown in step 101 above, and details are not described herein again.

[0045] Step 103, determine a plurality of preset metrics of the target user in each of the rooms to be matched according to the user feature information and the room feature information of each of the rooms to be matched.

[0046] In this step, the user feature information and the room feature information of each room to be matched can be input into a preset room matching model to obtain the multiple preset metrics output by the multiple classification modules in the preset room matching model, so as to obtain the multiple preset metrics of the target user in each room to be matched.

[0047] Among them, the preset room matching model includes multiple classification modules. Different classification modules are used to determine different preset metrics. The preset room matching model is used to determine the matching degree between the target user and each room to be matched according to the multiple preset metrics output by the multiple classification modules, and determine the target matching room from the multiple rooms to be matched according to the matching degree between the target user and each room to be matched.

[0048] It should be noted that the preset metrics may include at least one of a metric for measuring whether the player has completed the recommended game in the room, a metric for measuring whether the player will start the next game in the room, a metric for measuring the total number of game rounds completed in the room, a metric for measuring the proportion of the staying time in the current game round to the duration of the game round, a metric for measuring the number of game rounds completed by the player after finishing the current game round and within the next 24 hours. It may also include at least one of a metric for measuring the number of speeches of the player in the game room, a metric for measuring the number of comments generated in the room during the game process, a metric for measuring the positive degree of the comments made by the player during the game process, or a metric for measuring the number of likes obtained by the player during the game process. Each classification module in the preset room matching model is used to determine a preset metric according to the user feature information of the target user and the room feature information of each room to be matched.

[0049] In addition, it should be noted that the preset room matching model may be a machine learning model based on the LR (Logistic Regression) algorithm, neural network algorithm or tree algorithm.

[0050] Step 104: Sort the multiple rooms to be matched according to the multiple preset metrics corresponding to each room to be matched, so as to determine at least one target matching room.

[0051] In this step, the matching degree between the target user and each room to be matched is determined according to the multiple preset metrics corresponding to each room to be matched; the multiple rooms to be matched are sorted in descending order of the matching degree; and the preset number of rooms to be matched with the highest ranking are used as the target matching rooms.

[0052] Exemplarily, as Figure 2 shown, Figure 2 is a schematic diagram of a game room matching process shown in an exemplary embodiment of the present disclosure; in this Figure 2Among them, the portrait of Room 1 is the room feature information corresponding to Room 1, that is, Room 1 is described by the room feature information corresponding to Room 1. The portrait of Room 2 is the room feature information corresponding to Room 2, that is, Room 2 is described by the room feature information corresponding to Room 2. The portrait of Room 3 is the room feature information corresponding to Room 3, that is, Room 3 is described by the room feature information corresponding to Room 3. The user portrait is the user feature information, that is, the user is described by the user feature information. The LR prediction is to perform LR prediction through the preset room matching model (LR model) to score the user experience in multiple dimensions, that is, to obtain the index values of multiple preset indicators. The matching degree between the target user and each of the to-be-matched rooms is determined according to the multiple preset indicators corresponding to each of the to-be-matched rooms (that is, the score for each room is obtained), and the multiple to-be-matched rooms are sorted in descending order of the matching degree; the preset number of to-be-matched rooms with higher rankings are used as the target matching rooms of the target user. In addition, the framework of the algorithm matching in the present disclosure can be as Figure 3 shown Figure 3 is a schematic diagram of an algorithm matching framework shown in an exemplary embodiment of the present disclosure. The user feature information and room feature information can be collected by means of real-time data logging and offline data logging. When the player clicks "Quickly Join Room", the game client can send a request to the recommendation server. The server will first send a request to redis (database) to obtain the user feature information of the player and the room feature information of the idle rooms. Then, the features will be packaged and the packaged features will be used to access the model. Through the model algorithm, the list of rooms most suitable for the player to join will be predicted and returned to the game client so that the user can quickly join the game room.

[0053] In the above technical solution, the user feature information and the room feature information of each of the to-be-matched rooms are input into the preset room matching model to obtain at least one target matching room output by the preset room matching model; wherein, the preset room matching model includes multiple classification modules, and different classification modules are used to determine different preset indicators. The matching degree between the target user and each of the to-be-matched rooms can be determined according to the multiple preset indicators output by the multiple classification modules, and the target matching room can be determined from the multiple to-be-matched rooms according to the matching degree between the target user and each of the to-be-matched rooms. It is possible to provide a game room with a higher matching degree for the target user through multiple preset indicators, thereby effectively improving the game experience of game player users.

[0054] Figure 4 is according to the present disclosure Figure 1 shown embodiment shows a flowchart of a game room matching method; as Figure 4 shown, this method can be applied to the server, in Figure 1After sorting the multiple rooms to be matched according to the multiple preset metrics corresponding to each room to be matched in step 104 to determine at least one target matching room, the method may further include the following steps:

[0055] Step 105, sending the identification information of the at least one target matching room and the matching degree to the game client corresponding to the target user, so that the game client sends a target request to join a specified target matching room to the server according to the matching degree of each target matching room in the at least one target matching room.

[0056] In this step, in the case of including multiple target matching rooms, the target matching rooms may be sorted in descending order of the matching degree to generate a target matching room list, and then the target matching room list is sent to the game client, so that the game client sends a target request to join a specified target matching room to the server according to the target matching room list.

[0057] Step 106, in response to receiving the target request, obtaining the target number of people in the current specified target matching room.

[0058] Wherein, the target request may include the identification of the specified target matching room.

[0059] Step 107, determining whether the target number of people is less than a preset number threshold;

[0060] In this step, in the case of determining that the target number of people is less than the preset number threshold, step 108 is executed; in the case of determining that the target number of people is greater than or equal to the preset number threshold, step 109 is executed.

[0061] Step 108, adding the target user to the specified target matching room.

[0062] Step 109, determining whether the current specified target matching room is the target matching room with the lowest matching degree among the at least one target matching room.

[0063] In this step, if it is determined that the current specified target matching room is not the target matching room with the lowest matching degree among the at least one target matching room, step 110 is executed; if it is determined that the current specified target matching room is the target matching room with the lowest matching degree among the at least one target matching room, step 112 is executed.

[0064] Step 110: Send feedback information of failed joining to the game client, so that the game client, in the order of decreasing matching degree, uses the target matching room corresponding to the next matching degree of the current specified target matching room as the updated specified target matching room, and sends a target request to join the specified target matching room to the server.

[0065] Step 111: Determine whether a target request for the specified target matching room sent by the game client is received.

[0066] In this step, when it is determined that a target request for the specified target matching room sent by the game client is received, steps 106 to 107 above are executed again; when it is determined that a target request for the specified target matching room sent by the game client is not received, step 110 above is executed again.

[0067] Step 112: Generate a new target game room and add the target user to the target game room.

[0068] It should be noted that since the target request is sent to the server in the order of decreasing matching degree of the target matching room, when it is determined that the current specified target matching room is the target matching room with the lowest matching degree, and it is determined that there are no vacancies in other target matching rooms with a matching degree higher than the current specified target matching room. In this step, when it is determined that there are no vacancies in each target matching room, a new target game room is generated, so as to effectively improve the reliability of the target user to join the game room.

[0069] The content shown in steps 105 to 112 above can add the target user to the target matching room determined by the preset room matching model, and when it is determined that all current target matching rooms are full, generate a new target game room and add the target user to the target game room, which can not only ensure that users have a pleasant experience in the game room, but also effectively improve the reliability of the target user to join the game room, thereby further improving the user's game experience.

[0070] Figure 5 It is a flowchart of a model training method shown in an exemplary embodiment of the present disclosure; as Figure 5 shown, the training process of the preset room matching model may include the following steps:

[0071] Step S1: Obtain first sample data of multiple users and second sample data of multiple game rooms.

[0072] Among them, the first sample data includes at least one of user identity characteristics, user game-related attribute characteristics, user room preference characteristics, and user social attribute characteristics; the second sample data includes at least one of room configuration characteristics, user attribute characteristics, room game attribute characteristics, and room style characteristics.

[0073] It should be noted that for the specific descriptions of the user identity characteristics, user game-related attribute characteristics, user room preference characteristics, and user social attribute characteristics, reference can be made to the content shown in steps 101 and 102 above. Figure 1 The present disclosure will not elaborate here.

[0074] Step S2: Using the first sample data and the second sample data as training data, train a preset initial model to obtain the preset room matching model.

[0075] Among them, the preset initial model includes multiple initial classification modules, and different initial classification modules are used to determine different preset metrics.

[0076] It should be noted that the preset initial model can be an initial model of the LR algorithm, neural network algorithm, or tree algorithm. Among them, for the LR algorithm, neural network algorithm, and tree algorithm, reference can be made to the relevant descriptions in the prior art, and the present disclosure will not elaborate here.

[0077] In addition, it should also be noted that the model training process can be carried out online or offline. Then, place the trained preset room matching model into the server, and then directly through the above Figure 1 shown steps, obtain at least one target game room through the preset room matching model.

[0078] The above technical solution can effectively train a preset room matching model with strong generalization ability and better matching effect. Through the preset room matching model, a game room with a higher matching degree can be provided for the target user according to multiple preset metrics, thereby effectively improving the gaming experience of game player users.

[0079] Figure 6 It is a block diagram of a game room matching device shown in an exemplary embodiment of the present disclosure; as Figure 6 shown, the device may include:

[0080] The first acquisition module 401 is configured to receive a game room joining request of a target user and acquire the user feature information of the target user carried in the request;

[0081] The second acquisition module 402 is configured to acquire a plurality of rooms to be matched and the room feature information of each of the rooms to be matched;

[0082] The first determination module 403 is configured to determine multiple preset metrics of the target user in each of the rooms to be matched according to the user characteristic information and the room characteristic information of each of the rooms to be matched;

[0083] The second determination module 404 is configured to sort the multiple rooms to be matched according to the multiple preset metrics corresponding to each of the rooms to be matched, so as to determine at least one target matching room.

[0084] In the above technical solution, by receiving a game room join request of a target user and obtaining the user characteristic information of the target user carried in the request; obtaining multiple rooms to be matched and the room characteristic information of each of the rooms to be matched; determining multiple preset metrics of the target user in each of the rooms to be matched according to the user characteristic information and the room characteristic information of each of the rooms to be matched; sorting the multiple rooms to be matched according to the multiple preset metrics corresponding to each of the rooms to be matched, so as to determine at least one target matching room, it is possible to provide a game room with a higher matching degree for the target user through multiple preset metrics, thereby effectively improving the game experience of game player users.

[0085] Optionally, the second determination module 404 is configured to:

[0086] Determine the matching degree between the target user and each of the rooms to be matched according to the multiple preset metrics corresponding to each of the rooms to be matched;

[0087] Sort the multiple rooms to be matched in descending order of the matching degree;

[0088] Take the preset number of rooms to be matched with the top ranking as the target matching rooms.

[0089] Optionally, the first determination module 403 is configured to:

[0090] Input the user characteristic information and the room characteristic information of each of the rooms to be matched into a preset room matching model, where the preset room matching model includes multiple classification modules, and different classification modules are used to determine different preset metrics;

[0091] Obtain the multiple preset metrics output by the multiple classification modules in the preset room matching model.

[0092] Optionally, the second determination module 404 is configured to:

[0093] Obtain the preset weight corresponding to each of the preset metrics;

[0094] Weighted sum is performed on the multiple preset metrics corresponding to each of the to-be-matched rooms according to the preset weight corresponding to each of the preset metrics, so as to obtain the matching degree between the target user and the to-be-matched room.

[0095] Figure 7 is based on the present disclosure Figure 6 The block diagram of a game room matching device shown in the illustrated embodiment; as Figure 7 shown, the device is applied to a server, and the device may further include:

[0096] A sending module 405, configured to send the identification information of the at least one target matching room and the matching degree to the game client corresponding to the target user, so that the game client sends a target request to join a specified target matching room to the server according to the matching degree of each target matching room in the at least one target matching room;

[0097] A third obtaining module 406, configured to obtain the target number of people in the current specified target matching room in response to receiving the target request;

[0098] A third determining module 407, configured to determine whether the target number of people is less than a preset number threshold;

[0099] The third determining module 407 is configured to, when determining that the target number of people is less than the preset number threshold, add the target user to the specified target matching room.

[0100] Optionally, the third determining module 407 is further configured to:

[0101] When determining that the target number of people is greater than or equal to the preset number threshold, determine whether the current specified target matching room is the target matching room with the lowest matching degree among the at least one target matching room;

[0102] If it is determined that the current specified target matching room is not the target matching room with the lowest matching degree among the at least one target matching room, send feedback information of joining failure to the game client, so that the game client takes the target matching room corresponding to the next matching degree of the current specified target matching room according to the order of the matching degree from high to low as the updated specified target matching room, and send a target request to join the specified target matching room to the server; the server executes again the steps of obtaining the target number of people in the current specified target matching room in response to receiving the target request; and determining whether the target number of people is less than the preset number threshold;

[0103] If it is determined that the current specified target matching room is the target matching room with the lowest matching degree among the at least one target matching room, a new target game room is generated, and the target user is added to the target game room.

[0104] The above technical solution can add the target user to the target matching room determined according to the preset room matching model. When it is determined that all current target matching rooms are full, a new target game room is generated, and the target user is added to the target game room. This can not only ensure that users have a pleasant experience in the game room, but also effectively improve the reliability of the target user joining the game room, thereby further enhancing the user's game experience.

[0105] Optionally, the device further includes: a model training module 408, configured to:

[0106] Obtain first sample data of multiple users and second sample data of multiple game rooms;

[0107] Use the first sample data and the second sample data as training data to train a preset initial model to obtain the preset room matching model;

[0108] Wherein, the preset initial model includes multiple initial classification modules, and different initial classification modules are used to determine different preset metrics;

[0109] The first sample data includes at least one of user identity characteristics, user game category attribute characteristics, user room preference characteristics, and user social attribute characteristics; the second sample data includes at least one of room configuration characteristics, user attribute characteristics, room game attribute characteristics, and room style characteristics.

[0110] The above technical solution can effectively train a preset room matching model with strong generalization and better matching effect. Through the preset room matching model, game rooms with higher matching degrees can be provided for target users according to multiple preset metrics, thereby effectively improving the game experience of game player users.

[0111] Next, refer to Figure 8 , which shows a schematic structural diagram of an electronic device suitable for implementing the embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 8 The electronic device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present disclosure.

[0112] As shown Figure 8 in FIG. 1, the electronic device 600 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 601, which may perform various appropriate actions and processes according to the programs stored in the read-only memory (ROM) 602 or the programs loaded from the storage device 608 into the random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. The input / output (I / O) interface 605 is also connected to the bus 604.

[0113] Generally, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 8 FIG. 1 shows the electronic device 600 having various devices, it should be understood that it is not required to implement or have all the shown devices. Instead, more or fewer devices may be implemented or had.

[0114] Specifically, according to an embodiment of the present disclosure, the process described above with reference to the flowchart may be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes program codes for performing the methods shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above functions defined in the method of the embodiment of the present disclosure are executed.

[0115] It should be noted that the computer-readable medium described above can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, apparatus, or device. In the present disclosure, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which the computer-readable program code is carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and the computer-readable signal medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0116] In some embodiments, the server can communicate using any currently known or future-developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0117] The above computer-readable medium can be included in the above electronic device; or it can exist separately without being assembled into the electronic device.

[0118] The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by the electronic device, the electronic device is caused to:

[0119] Receive a game room join request from a target user and obtain the user feature information of the target user carried in the request;

[0120] Obtain multiple rooms to be matched and the room feature information of each of the rooms to be matched;

[0121] Determine multiple preset metrics of the target user in each of the rooms to be matched according to the user feature information and the room feature information of each of the rooms to be matched;

[0122] Sort the multiple rooms to be matched according to the multiple preset metrics corresponding to each of the rooms to be matched to determine at least one target matching room.

[0123] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include, but are not limited to, object-oriented programming languages - such as Java, Smalltalk, C++; and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network - including a local area network (LAN) or a wide area network (WAN) - or may be connected to an external computer (for example, by connecting through the Internet service provider via the Internet).

[0124] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0125] The modules involved in the embodiments of the present disclosure can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation to the module itself in some cases. For example, the first acquisition module can also be described as "receiving a game room joining request of a target user and acquiring user feature information of the target user carried in the request".

[0126] The functions described above herein can be performed, at least in part, by one or more hardware logic components. By way of example, and without limitation, exemplary types of hardware logic components that may be used include: Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), Application Specific Standard Product (ASSP), System on Chip (SOC), Complex Programmable Logic Device (CPLD), and so on.

[0127] In the context of the present disclosure, a machine-readable medium may be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM or Flash Memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0128] According to one or more embodiments of the present disclosure, Example 1 provides a game room matching method, the method comprising:

[0129] Receiving a game room joining request of a target user and acquiring user feature information of the target user carried in the request;

[0130] Acquiring a plurality of rooms to be matched and room feature information of each of the rooms to be matched;

[0131] Determining a plurality of preset metrics of the target user in each of the rooms to be matched according to the user feature information and the room feature information of each of the rooms to be matched;

[0132] Sorting the plurality of rooms to be matched according to the plurality of preset metrics corresponding to each of the rooms to be matched to determine at least one target matching room.

[0133] According to one or more embodiments of the present disclosure, Example 2 provides the method of Example 1,

[0134] sorting the multiple rooms to be matched according to the multiple preset metrics corresponding to each room to be matched to determine at least one target matching room, including:

[0135] determining the matching degree between the target user and each room to be matched according to the multiple preset metrics corresponding to each room to be matched;

[0136] sorting the multiple rooms to be matched in descending order of the matching degree;

[0137] taking the preset number of rooms to be matched with the top rankings as the target matching rooms.

[0138] According to one or more embodiments of the present disclosure, Example 3 provides the method of Example 1, and determining the multiple preset metrics of the target user in each room to be matched according to the user characteristic information and the room characteristic information of each room to be matched includes:

[0139] inputting the user characteristic information and the room characteristic information of each room to be matched into a preset room matching model, where the preset room matching model includes multiple classification modules, and different classification modules are used to determine different preset metrics;

[0140] obtaining the multiple preset metrics output by the multiple classification modules in the preset room matching model.

[0141] According to one or more embodiments of the present disclosure, Example 4 provides the method of Example 2, and determining the matching degree between the target user and each room to be matched according to the multiple preset metrics corresponding to each room to be matched includes:

[0142] obtaining the preset weight corresponding to each preset metric;

[0143] performing weighted summation on the multiple preset metrics corresponding to each room to be matched according to the preset weight corresponding to each preset metric to obtain the matching degree between the target user and the room to be matched.

[0144] According to one or more embodiments of the present disclosure, Example 5 provides the method of Example 4,

[0145] when the method is applied to a server, after determining at least one target matching room, the method further includes:

[0146] Send the identification information of the at least one target matching room and the matching degree to the game client corresponding to the target user, so that the game client sends a target request to join a specified target matching room to the server according to the matching degree of each target matching room in the at least one target matching room;

[0147] In response to receiving the target request, obtain the target number of the current specified target matching room;

[0148] Determine whether the target number is less than a preset number threshold;

[0149] In the case where it is determined that the target number is less than the preset number threshold, add the target user to the specified target matching room.

[0150] According to one or more embodiments of the present disclosure, Example 6 provides the method of Example 5, and the method further includes:

[0151] In the case where it is determined that the target number is greater than or equal to the preset number threshold, determine whether the current specified target matching room is the target matching room with the lowest matching degree among the at least one target matching room;

[0152] If it is determined that the current specified target matching room is not the target matching room with the lowest matching degree among the at least one target matching room, send feedback information of joining failure to the game client, so that the game client takes the target matching room corresponding to the next matching degree of the current specified target matching room as the updated specified target matching room according to the order of the matching degrees from high to low, and sends a target request to join the specified target matching room to the server; the server executes again the steps of in response to receiving the target request, obtaining the target number of the current specified target matching room; determining whether the target number is less than the preset number threshold;

[0153] If it is determined that the current specified target matching room is the target matching room with the lowest matching degree among the at least one target matching room, generate a new target game room and add the target user to the target game room.

[0154] According to one or more embodiments of the present disclosure, Example 7 provides the method shown in Example 1,

[0155] The room feature information includes at least one of room configuration features, room user attribute features, room game attribute features, and room style features;

[0156] Among them, the room style features may include one or more of the frequency of adding new users within a historical time period, the user change frequency, and the proportion of users waiting for the start of the next game round.

[0157] According to one or more embodiments of the present disclosure, Example 8 provides the method shown in Example 3. The preset room matching model is trained by the following method:

[0158] Obtain first sample data of multiple users and second sample data of multiple game rooms;

[0159] Use the first sample data and the second sample data as training data to train a preset initial model to obtain the preset room matching model;

[0160] Among them, the preset initial model includes multiple initial classification modules, and different initial classification modules are used to determine different preset metrics;

[0161] The first sample data includes at least one of user identity characteristics, user game category attribute characteristics, user room preference characteristics, and user social attribute characteristics; the second sample data includes at least one of room configuration characteristics, user attribute characteristics, room game attribute characteristics, and room style characteristics.

[0162] According to one or more embodiments of the present disclosure, Example 9 provides a game room matching device. The device includes: a first acquisition module configured to receive a game room joining request of a target user and acquire user feature information of the target user carried in the request;

[0163] A second acquisition module configured to acquire multiple rooms to be matched and room feature information of each of the rooms to be matched;

[0164] A first determination module configured to determine multiple preset metrics of the target user in each of the rooms to be matched according to the user feature information and the room feature information of each of the rooms to be matched;

[0165] A second determination module configured to sort the multiple rooms to be matched according to the multiple preset metrics corresponding to each of the rooms to be matched to determine at least one target matching room.

[0166] According to one or more embodiments of the present disclosure, Example 10 provides a computer-readable medium having a computer program stored thereon, and when the program is executed by a processing device, the steps of the method described in any one of Examples 1-8 above are implemented.

[0167] According to one or more embodiments of the present disclosure, Example 11 provides an electronic device, including:

[0168] A storage device on which a computer program is stored;

[0169] A processing device for executing the computer program in the storage device to implement the steps of the method according to any one of the above Examples 1-8.

[0170] The above description is only a preferred embodiment of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the present disclosure.

[0171] In addition, although the operations are depicted in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or in sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although a number of specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, the various features described in the context of a single embodiment may also be implemented separately or in any suitable sub-combination in multiple embodiments.

[0172] Although the subject matter has been described in language specific to structural features and / or method logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. On the contrary, the specific features and acts described above are merely example forms for implementing the claims. Regarding the device in the above embodiments, the specific manner in which each module performs the operations has been described in detail in the embodiments related to the method, and will not be elaborated here.

Claims

1. A game room matching method, characterized in that, The method includes: Receiving a game room joining request from a target user and obtaining user feature information of the target user carried in the request; Obtaining a plurality of rooms to be matched and room feature information of each of the rooms to be matched; Inputting the user feature information and the room feature information of each of the rooms to be matched into a preset room matching model, where the preset room matching model includes a plurality of classification modules, and different classification modules are used to determine different preset metrics; Obtaining a plurality of preset metrics output by the plurality of classification modules in the preset room matching model; Sorting the plurality of rooms to be matched according to the plurality of preset metrics corresponding to each of the rooms to be matched to determine at least one target matching room.

2. The method according to claim 1, wherein The sorting the plurality of rooms to be matched according to the plurality of preset metrics corresponding to each of the rooms to be matched to determine at least one target matching room includes: Determining the matching degree between the target user and each of the rooms to be matched according to the plurality of preset metrics corresponding to each of the rooms to be matched; Sorting the plurality of rooms to be matched in descending order of the matching degree; Taking the preset number of the rooms to be matched with the highest ranking as the target matching rooms.

3. The method according to claim 2, wherein The determining the matching degree between the target user and each of the rooms to be matched according to the plurality of preset metrics corresponding to each of the rooms to be matched includes: Obtaining a preset weight corresponding to each of the preset metrics; Performing weighted summation on the plurality of preset metrics corresponding to each of the rooms to be matched according to the preset weight corresponding to each of the preset metrics to obtain the matching degree between the target user and the room to be matched.

4. The method according to claim 2, wherein The method is applied to a server. After determining at least one target matching room, the method further includes: Sending the identification information of the at least one target matching room and the matching degree to the game client corresponding to the target user, so that the game client sends a target request to join a specified target matching room to the server according to the matching degree of each target matching room in the at least one target matching room; In response to receiving the target request, obtaining the target number of people in the current specified target matching room; Determining whether the target number of people is less than a preset number threshold; In the case where it is determined that the target number of people is less than the preset number threshold, adding the target user to the specified target matching room.

5. The method according to claim 4, wherein The method further includes: In the case where it is determined that the target number of people is greater than or equal to the preset number threshold, determining whether the current specified target matching room is the target matching room with the lowest matching degree among the at least one target matching room; If it is determined that the current specified target matching room is the target matching room with the lowest matching degree among the at least one target matching rooms, a feedback message of failed joining is sent to the game client, so that the game client takes the target matching room corresponding to the next matching degree of the current specified target matching room as the updated specified target matching room in the order of the matching degree from high to low, and sends a target request to join the specified target matching room to the server; the server executes again the steps of obtaining the target number of the current specified target matching room in response to receiving the target request; and determining whether the target number is less than the preset number threshold. If it is determined that the current specified target matching room is the target matching room with the lowest matching degree among the at least one target matching rooms, a new target game room is generated, and the target user is added to the target game room.

6. The method according to claim 1, wherein The room feature information includes at least one of room configuration features, room user attribute features, room game attribute features, and room style features. Among them, the room style feature may include one or more of the frequency of adding new users within a historical time period, the user change frequency, and the proportion of users waiting for the start of the next game.

7. The method according to claim 1, characterized in that, The preset room matching model is trained by the following method: Obtain the first sample data of multiple users and the second sample data of multiple game rooms. Using the first sample data and the second sample data as training data, train a preset initial model to obtain the preset room matching model. Among them, the preset initial model includes multiple initial classification modules, and different initial classification modules are used to determine different preset metrics. The first sample data includes user feature data; the second sample data includes at least one of room configuration features, user attribute features, room game attribute features, and room style features.

8. A game room matching device, characterized in that, The device includes: A first acquisition module, configured to receive a game room joining request of a target user and obtain the user feature information of the target user carried in the request. A second acquisition module, configured to obtain multiple rooms to be matched and the room feature information of each room to be matched. A first determination module, configured to input the user feature information and the room feature information of each room to be matched into a preset room matching model, where the preset room matching model includes multiple classification modules, and different classification modules are used to determine different preset metrics; and obtain multiple preset metrics output by the multiple classification modules in the preset room matching model. A second determination module, configured to sort the multiple rooms to be matched according to the multiple preset metrics corresponding to each room to be matched to determine at least one target matching room.

9. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by a processing device, it implements the steps of the method according to any one of claims 1-7.

10. An electronic device, characterized in that, Including: A storage device, on which a computer program is stored. A processing device for executing the computer program in the storage device to implement the steps of the method according to any one of claims 1 to 7.

Images