KR20230114422A - Method for predicting of preference - Google Patents

Abstract

The present invention relates to a method for predicting a preference level. As the method for predicting the preference level performed by a computing apparatus including equal to or more than one processor according to some embodiments of the present disclosure, the method comprises a step of inputting new game log data into a preference level predicting model to generate result data related to the preference level. The preference level predicting model can be learned using training data in which a preference level value is labeled in pre-stored game log data.

Description

Method for predicting preference {METHOD FOR PREDICTING OF PREFERENCE}

The present disclosure relates to artificial intelligence, and more particularly, to a method for predicting a preference using an artificial intelligence-based preference prediction model.

In recent years, machine-learning technologies including deep-learning have been attracting attention by showing results that exceed the performance of existing methods in analyzing various types of data such as video, voice, and text. In addition, machine learning technology has been introduced and utilized in various fields due to the scalability and flexibility inherent in the technology itself.

Among various fields to which machine learning technology can be applied, the game field corresponds to one of the fields in which machine learning technology is most actively introduced to recommend games to users.

In order for a machine learning model to be properly used in a specific domain, training data with fewer errors is required, and gaps in the data must be small in order to acquire training data with fewer errors.

Republic of Korea Patent Publication No. 10-2000-0030786 (published on December 12, 2001)

The present disclosure has been made in response to the above background art, and intends to predict preferences using a preference prediction model.

The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

According to some embodiments of the present disclosure for solving the above problems, a method for predicting preferences performed by a computing device including one or more processors, the method comprising: converting new game log data to a preference prediction model. and generating result data related to the preference; and the preference prediction model may be learned using learning data in which preference values are labeled in pre-stored game log data.

Alternatively, the pre-stored game log data may include information about a plurality of games, information about a plurality of game properties, information about a plurality of users, and information about access to the plurality of games.

Alternatively, the plurality of game properties may be related to at least one of a game category, intellectual property (IP), gameplay, graphics, business model, social function, or genre of the game world. can be determined based on

Alternatively, at least one game attribute may be assigned to each of the plurality of games, and at least one game corresponding to each of the plurality of game attributes may be tagged.

Alternatively, the preference value may include a plurality of sub-preference values for each of a plurality of game attributes of a plurality of users.

Alternatively, the plurality of sub preference values may be calculated based on information about connection of a plurality of games and similarity values of the plurality of game attributes.

Alternatively, the similarity value may include sub-similarity values that are similar degrees between the plurality of game attributes.

Alternatively, the sub similarity values may be calculated through a collaborative filtering algorithm based on at least one first game log data among the pre-stored game log data.

Alternatively, the at least one first game log data may be sampled based on information on access of the plurality of games.

Alternatively, it is possible to determine whether the user has a preference for a specific game included in the new game log data based on result data related to the preference.

As a computer program stored in a computer readable storage medium according to some other embodiments of the present disclosure for solving the above problem, the computer program causes a processor of a computing device to predict a preference to perform the following steps It includes instructions for: inputting new game log data into a preference prediction model to generate result data related to a preference; Including, the preference prediction model can be learned using training data in which preference values are labeled in pre-stored game log data.

A computing device for predicting a preference according to some other embodiments of the present disclosure for solving the above problem, comprising: a processor; a memory storing a computer program executable by the processor; and a network unit, inputting new game log data into a preference prediction model to generate result data related to preference, wherein the preference prediction model uses learning data in which preference values are labeled in pre-stored game log data. so it can be learned.

The present disclosure can predict a user's preference in a more accurate manner using a preference prediction model.

Effects obtainable in the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below. .

Various aspects are now described with reference to the drawings, wherein like reference numbers are used to collectively refer to like elements. In the following embodiments, for explanation purposes, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. However, it will be apparent that such aspect(s) may be practiced without these specific details.
1 is a block diagram of a computing device for predicting preferences according to some embodiments of the present disclosure.
2 is a schematic diagram illustrating a neural network according to some embodiments of the present disclosure.
3 is a diagram illustrating a method for predicting preferences performed in a computing device according to some embodiments of the present disclosure.
4 is a diagram illustrating a method of calculating a plurality of sub-preference values performed by a computing device according to some embodiments of the present disclosure.
5 depicts a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the present disclosure. However, it is apparent that these embodiments may be practiced without these specific details.

The terms “component,” “module,” “system,” and the like, as used herein, refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or an execution of software. For example, a component may be, but is not limited to, a procedure, processor, object, thread of execution, program, and/or computer running on a processor. For example, both an application running on a computing device and a computing device may be components. One or more components may reside within a processor and/or thread of execution. A component can be localized within a single computer. A component may be distributed between two or more computers. Also, these components can execute from various computer readable media having various data structures stored thereon. Components may be connected, for example, via signals with one or more packets of data (e.g., data and/or signals from one component interacting with another component in a local system, distributed system) to other systems and over a network such as the Internet. data being transmitted) may communicate via local and/or remote processes.

In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless otherwise specified or clear from the context, “X employs A or B” is intended to mean one of the natural inclusive substitutions. That is, X uses A; X uses B; Or, if X uses both A and B, "X uses either A or B" may apply to either of these cases. Also, the term "and/or" as used herein should be understood to refer to and include all possible combinations of one or more of the listed related items.

Also, the terms "comprises" and/or "comprising" should be understood to mean that the features and/or components are present. However, it should be understood that the terms "comprises" and/or "comprising" do not exclude the presence or addition of one or more other features, elements, and/or groups thereof. Also, unless otherwise specified or where the context clearly indicates that a singular form is indicated, the singular in this specification and claims should generally be construed to mean "one or more".

In addition, the term “at least one of A or B” should be interpreted as meaning “when only A is included”, “when only B is included”, and “when A and B are combined”.

Those skilled in the art will further understand that the various illustrative logical blocks, components, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented using electronic hardware, computer software, or combinations of both. It should be recognized that it can be implemented as To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or as software depends on the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of this disclosure.

The description of the presented embodiments is provided to enable any person skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art of this disclosure. The general principles defined herein may be applied to other embodiments without departing from the scope of this disclosure. Thus, the present invention is not limited to the embodiments presented herein. The present invention is to be accorded the widest scope consistent with the principles and novel features set forth herein.

1 is a block diagram of a computing device for predicting preferences according to some embodiments of the present disclosure.

The configuration of the computing device 100 shown in FIG. 1 is only a simplified example. In one embodiment of the present disclosure, the computing device 100 may include other components for performing a computing environment of the computing device 100, and only some of the disclosed components may constitute the computing device 100.

The computing device 100 may be a device for predicting preferences using an AI-based preference prediction model. The computing device 100 may include any type of server or user terminal.

The computing device 100 may include a processor 110 , a memory 130 , and a network unit 150 .

The processor 110 may typically control overall operations of the computing device 100 . The processor 110 provides or processes appropriate information or functions by processing signals, data, information, etc. input or output through components included in the computing device 100 or by running an application program stored in the memory 130. can do. Also, the processor 110 may control at least some of the components of the computing device 100 in order to drive an application program stored in the memory 130 . Furthermore, the processor 110 may combine and operate at least two or more of the components included in the computing device 100 to drive the application program.

The processor 110 may include one or more cores, and includes a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. unit), data analysis, and processors for deep learning. The processor 110 may read a computer program stored in the memory 130 and process data for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor 110 may perform an operation for learning a neural network. The processor 110 is used for neural network learning, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating neural network weights using backpropagation. calculations can be performed. At least one of the CPU, GPGPU, and TPU of the processor 110 may process learning of the network function. For example, the CPU and GPGPU can process learning of network functions and data classification using network functions. In addition, in an embodiment of the present disclosure, the learning of a network function and data classification using a network function may be processed by using processors of a plurality of computing devices together. In addition, a computer program executed in a computing device according to an embodiment of the present disclosure may be a CPU, GPGPU or TPU executable program.

Meanwhile, the processor 110 may generate result data related to the preference by inputting the new game log data to the preference prediction model.

The new game log data may be game log data not used for learning the preference prediction model. For example, the new game log data may include at least one of information about a new game, information about game attributes corresponding to the new game, information about users who have played the new game, and/or information about users' access to the new game. may contain one. For another example, the new game log data may include information about a new user who has played an existing game.

The preference prediction model may include a neural network for generating result data related to preference based on input game log data. A detailed description of the neural network will be described later with reference to FIG. 2 . The preference prediction model may be learned using training data in which preference values are labeled in pre-stored game log data.

The pre-stored game log data may include information about a plurality of games, information about a plurality of game attributes, information about a plurality of users, and/or information about access to the plurality of games. According to some embodiments of the present disclosure, the processor 110 may obtain game log data of users who have played a game for a predetermined period of time from among pre-stored game log data. For example, the processor 110 may acquire all game log data from pre-stored game log data, or acquire only game log data for users with game play records for the past month. can Accordingly, the processor 110 may use game log data of users who have played a game for a predetermined period among pre-stored game log data to generate learning data. Also, the processor 110 may assign at least one game property to each of a plurality of games. According to some other embodiments of the present disclosure, at least one game property may be previously assigned to each of a plurality of games. The processor 110 may tag at least one game corresponding to at least one game property.

Information on a plurality of games includes information on a plurality of game attributes corresponding to each of the plurality of games, information on a plurality of users corresponding to each of the plurality of games, and information on a connection corresponding to each of the plurality of games. information may be included.

Information on a plurality of users may include data related to the user's game play. For example, the information of a plurality of users may include data on the number of stages cleared, the number of killing other players, the number of rankings according to game play, and the like.

Information about access to a plurality of games may include data about game play of a plurality of users. For example, information about access to a plurality of games may include data about game play time, game access time, and game exit time of each of a plurality of users.

The plurality of game properties may be determined based on at least one of a game category, intellectual property (IP), progression method, graphic, business model, social function, and/or game world genre. there is. That is, the processor 110 may determine a plurality of game properties based on at least one of a game category, intellectual property rights, playing method, graphics, business model, social function, and/or game world genre. For example, the processor 110 assigns a plurality of game properties corresponding to each of a plurality of games to at least one of game categories, intellectual property rights, game modes, graphics, business models, social functions, and/or game world genres. can be determined based on

The game category may relate to a type of game. For example, categories of games include puzzle games, board games, role-playing games (RPG), management simulation games, strategy games, and massive multiplayer online role-playing games (MMORPG). ), training simulation game, side-scrolling action game, racing game, sports game, vertical scrolling action game, social network game (SNG), action role-playing game, gun shooting game, music game, AOS game, etc. can do.

Intellectual property rights of games may be related to original works on which the games are based. For example, intellectual property rights of games may include original games, novels, cartoons, animations, characters, webtoons, and the like.

The way the game progresses may relate to the way the game is progressed by the user within the game. For example, the game progress method may include manual battle, battle, automatic battle, turn-based, and the like.

The graphics of the game may relate to the dimension and/or style of the game's background, characters, objects, and the like. For example, game graphics may include 2D, 3D, live action, animation, and the like.

A game's business model can be a way to earn money through games. For example, a business model of a game may include cash sales, item sales, character sales, and the like.

The social function of the game may be a function using users connected through a specific social network in the game. For example, the social function of the game may include a reward-type friend invitation function, a stamina gift function, a gift function, a sharing function, and the like. A social network may be a social relationship structure in which individuals or groups form a personal relationship on the Internet.

The genre of the game world may be a genre applied within the game. For example, the genre of the game world may include modern, fantasy, military, and martial arts. The genre of the game world may include a genre corresponding to the original work of intellectual property rights.

Meanwhile, result data related to preference may include at least one game property and preference value. When there are a plurality of game attributes, result data related to preference may include a plurality of game attributes and preference values.

The preference value may be an indicator quantifying how much a user prefers a specific game property. For example, the preference value may be an access probability or a preference score for at least one game obtained based on game log data. The preference value may include a plurality of sub-preference values (eg, 20 points, 30 points, etc.) for each of a plurality of game attributes of a plurality of users. For example, the preference values include a first user's first sub-preference value for 'puzzle game', a first user's second sub-preference value for 'music game', and a second user's second user's second sub-preference value for 'puzzle game'. 3 sub-preference values, a 4th sub-preference value for 'music game' of the second user, and the like.

The plurality of sub-preference values may be scores indicating the degree of preference of a plurality of users for each of a plurality of game attributes. A plurality of sub preference values may be calculated based on information of a plurality of users and similarity values of a plurality of game attributes. That is, the processor 110 may calculate a plurality of sub preference values based on information about access to a plurality of games and similarity values of a plurality of game attributes. For example, the processor 110 may calculate a plurality of sub-preference values using game play data of a plurality of users included in information about access to a plurality of games and similarity values of a plurality of game attributes.

The similarity value may be an index that quantifies the degree of similarity between game attributes. The similarity value may include sub-similarity values that are similar degrees between a plurality of game attributes.

The sub similarity values may be calculated through a recommendation algorithm based on at least one first game log data among pre-stored game log data. That is, the processor 110 may calculate sub similarity values through a recommendation algorithm based on at least one first game log data among pre-stored game log data. The recommendation algorithm may include a collaborative filtering algorithm. The collaborative filtering algorithm may be a method using similarities between users or contents.

The collaborative filtering algorithm may include a collaborative filtering algorithm between users and a collaborative filtering algorithm between contents.

The collaborative filtering algorithm between users may determine sub-similarity values using similarities between users. Collaborative filtering algorithm between users classifies users who show similar patterns into groups using information about users (eg, game log data of users), and then uses information of other users included in the same group to calculate sub-similarity values. can determine them.

The collaborative filtering algorithm between contents may determine sub-similarity values using contents (eg, games) simultaneously used by the user. For example, if the games played by the first user are the first game and the second game, the algorithm for collaborative filtering between contents determines that the second user who played the first game will also play the second game, and determines that the game has a plurality of attributes. It is possible to determine sub-similarity values, which are degrees of similarity between them.

At least one first game log data may be sampled based on information about access of a plurality of games. That is, the processor 110 may sample at least one first game log data based on information on access of a plurality of games. For example, the processor 110 samples game log data of users who have played a predetermined number of games (eg, 10) or more for a predetermined period of time (eg, 100 hours) to obtain first game log data. can be obtained

As described above, the processor 110 may generate result data related to the preference by inputting the game log data to the preference prediction model trained using the learning data in which the preference value is labeled in the pre-stored game log data. Accordingly, the processor 110 may generate result data related to a preference including a preference value for each game property of a specific user by using a preference prediction model.

Meanwhile, the processor 110 may determine whether the user prefers a specific game (eg, the first game, the second game, etc.) included in the new game log data based on the result data related to the preference. For example, the processor 110 may determine whether the first user prefers the first game. The processor 110 may also determine whether a user prefers a game for which there is no access record.

Specifically, the processor 110 may recognize at least one game property corresponding to a specific game. For example, the processor 110 may recognize 'puzzle game' as at least one game property corresponding to the first game.

The processor 110 may obtain a plurality of sub preference values corresponding to at least one recognized game attribute among the user's preference values included in the new game log data. For example, when the recognized game attribute is 'puzzle game', the processor 110 may obtain the first user's sub-preference value included in new game log data for 'puzzle game'.

The processor 110 may determine whether the user included in the new game log data has a preference for a specific game based on the user's sub-preference value included in the new game log data. For example, if the sub-preference value of the user included in the new game log data is greater than a predetermined threshold value (eg, 50 points), the processor 110 may assign the user included in the new game log data to a specific game. can be judged as a preference for For another example, the processor 110 determines that the user included in the new game log data is assigned to a specific game when the user's sub-preference value included in the new game log data is smaller than a predetermined threshold value (eg, 50 points). It can be judged that there is no preference for .

When there are a plurality of sub-preference values of the user included in the new game log data, the processor 110 compares the sum of the plurality of sub-preference values with a preset threshold value to determine whether a specific user prefers a specific game. can judge However, the method of determining future access is not limited to this, and the processor 110 compares various values such as the average and maximum values of a plurality of sub-preference values with a predetermined threshold value, and the new game log data included in the game log data. It is possible to determine whether the user has a preference for a specific game.

Meanwhile, according to some embodiments of the present disclosure, the processor 110 inputs result data related to preference and information on game properties of a specific game to a classification model to assign a user's specific game included in the new game log data. preference can be judged.

The classification model may calculate a final preference value based on the result data and attributes of a particular game. For example, the classification model may calculate a final preference value by adding up sub-preference values corresponding to properties of a specific game.

Further, the classification model may determine whether the user has a preference for a specific game included in the new game log data based on a final preference value based on a predetermined threshold value.

Meanwhile, a process of generating learning data for learning a preference prediction model in the processor 110 will be described later.

The processor 110 may assign at least one game property to each of a plurality of games. The processor 110 may tag at least one game corresponding to at least one game attribute. When there are a plurality of game properties, the processor 110 may tag at least one game corresponding to each of the plurality of game properties. Accordingly, at least one game attribute may be assigned to each of the plurality of games, and at least one game corresponding to each of the plurality of game attributes may be tagged.

For example, when a new game is added, the processor 110 may assign at least one game property corresponding to the new game, and the new game may be tagged to the at least one game property. For example, when it is determined that the added first game is related to a puzzle, the processor 110 may assign 'puzzle game' as a game property to the first game. In addition, the first game may be tagged as 'puzzle game', which is a game property. Therefore, even if a new game is added, the processor 110 can reduce data voids and reduce the amount of calculations during data processing by tagging the new game to existing game properties without adding a column for the new game.

The processor 110 may calculate sub similarity values through a collaborative filtering algorithm based on the sampled first game log data. For example, the processor 110 may generate a similarity matrix including sub-similarity values that are similar degrees between a plurality of game attributes based on the sampled first game log data.

Accordingly, since the processor 110 calculates all sub-similarity values through a collaborative filtering algorithm based on the sampled first game log data, errors caused by data gaps can be reduced.

The processor 110 may calculate a plurality of sub-preference values for each of a plurality of game attributes of a plurality of users based on similarity values including the plurality of calculated sub-similarity values and information on connection of a plurality of games. there is. Accordingly, the processor 110 may calculate sub-preference values for each of all game attributes of all users included in the pre-stored game log data.

The processor 110 may generate learning data based on pre-stored game log data and preference values including sub-preference values. For example, the processor 110 may generate learning data by labeling preference values to pre-stored game log data.

The processor 110 may train a preference prediction model using the generated learning data.

According to some embodiments of the present disclosure, the memory 130 may store any type of information generated or determined by the processor 110 and any type of information received by the network unit 150 . For example, the memory 130 may store game log data, new game log data, learning data, and result data. However, it is not limited thereto, and the memory 130 may store various types of information.

According to an embodiment of the present disclosure, the memory 130 is a flash memory type, a hard disk type, a multimedia card micro type, or a card type memory (eg, SD or XD memory, etc.), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory) -Only Memory), a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium. The computing device 100 may operate in relation to a web storage that performs a storage function of the memory 130 on the Internet. The above description of the memory is only an example, and the present disclosure is not limited thereto.

The network unit 150 according to some embodiments of the present disclosure may include an arbitrary wired/wireless communication network capable of transmitting and receiving data and signals of any type, and may be included in the network represented in the present disclosure.

The techniques described herein may be used in the networks mentioned above as well as other networks.

As described above, even if a new game is added, the computing device 100 according to the present disclosure does not add a column for the new game and tags the new game to the existing game properties, thereby reducing data gaps and processing data. The amount of computation can be reduced.

In addition, the existing method of calculating preference using the similarity between games is complicated and takes a long time to calculate the similarity between all games when a new game is added. However, the computing device 100 according to the present disclosure calculates and uses the similarity between game properties, so that users' preference values for a new game can be quickly obtained by identifying and adding only the game properties for a new game. Accordingly, the computing device 100 may determine whether or not users prefer a new game.

2 is a schematic diagram illustrating a neural network according to an embodiment of the present disclosure. As described above, the neural network may be included in the preference prediction model.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. A neural network may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network includes one or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more links.

In a neural network, one or more nodes connected through a link may form a relative relationship of an input node and an output node. The concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa. As described above, an input node to output node relationship may be created around a link. More than one output node can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of data of the output node may be determined based on data input to the input node. Here, a link interconnecting an input node and an output node may have a weight. The weight may be variable, and may be changed by a user or an algorithm in order to perform a function desired by the neural network. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. An output node value may be determined based on the weight.

As described above, in the neural network, one or more nodes are interconnected through one or more links to form an input node and output node relationship in the neural network. Characteristics of the neural network may be determined according to the number of nodes and links in the neural network, an association between the nodes and links, and a weight value assigned to each link. For example, when there are two neural networks having the same number of nodes and links and different weight values of the links, the two neural networks may be recognized as different from each other.

A neural network may be composed of a set of one or more nodes. A subset of nodes constituting a neural network may constitute a layer. Some of the nodes constituting the neural network may form one layer based on distances from the first input node. For example, a set of nodes having a distance of n from the first input node may constitute n layers. The distance from the first input node may be defined by the minimum number of links that must be passed through to reach the corresponding node from the first input node. However, the definition of such a layer is arbitrary for explanation, and the order of a layer in a neural network may be defined in a method different from the above. For example, a layer of nodes may be defined by a distance from a final output node.

An initial input node may refer to one or more nodes to which data is directly input without going through a link in relation to other nodes among nodes in the neural network. Alternatively, in a relationship between nodes based on a link in a neural network, it may mean nodes that do not have other input nodes connected by a link. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the neural network. Also, the hidden node may refer to nodes constituting the neural network other than the first input node and the last output node.

In the neural network according to an embodiment of the present disclosure, the number of nodes in the input layer may be the same as the number of nodes in the output layer, and the number of nodes decreases and then increases again as the number of nodes progresses from the input layer to the hidden layer. can In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes of the input layer may be less than the number of nodes of the output layer and the number of nodes decreases as the number of nodes increases from the input layer to the hidden layer. there is. In addition, the neural network according to another embodiment of the present disclosure is a neural network in which the number of nodes in the input layer may be greater than the number of nodes in the output layer, and the number of nodes increases as the number of nodes increases from the input layer to the hidden layer. can A neural network according to another embodiment of the present disclosure may be a neural network in the form of a combination of the aforementioned neural networks.

A deep neural network (DNN) may refer to a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can reveal latent structures in data. In other words, it can identify the latent structure of a photo, text, video, sound, or music (e.g., what objects are in the photo, what the content and emotion of the text are, what the content and emotion of the audio are, etc.). . Deep neural networks include a convolutional neural network (CNN), a recurrent neural network (RNN), an auto encoder, a restricted boltzmann machine (RBM), and a deep trust network ( It may include a deep belief network (DBN), a Q network, a U network, a Siamese network, a Generative Adversarial Network (GAN), and the like. The description of the deep neural network described above is only an example, and the present disclosure is not limited thereto.

The neural network may be trained using at least one of supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Learning of the neural network may be a process of applying knowledge for the neural network to perform a specific operation to the neural network.

A neural network can be trained in a way that minimizes output errors. In the learning of the neural network, the learning data is repeatedly input into the neural network, the output of the neural network for the training data and the error of the target are calculated, and the error of the neural network is transferred from the output layer of the neural network to the input layer in the direction of reducing the error. It is a process of updating the weight of each node of the neural network by backpropagating in the same direction. In the case of teacher learning, the learning data in which the correct answer is labeled is used for each learning data (ie, the labeled learning data), and in the case of comparative teacher learning, the correct answer may not be labeled in each learning data. That is, for example, learning data in the case of teacher learning regarding data classification may be data in which each learning data is labeled with a category. Labeled training data is input to a neural network, and an error may be calculated by comparing an output (category) of the neural network and a label of the training data. As another example, in the case of comparative history learning for data classification, an error may be calculated by comparing input learning data with a neural network output. The calculated error is back-propagated in a reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back-propagation. The amount of change in the connection weight of each updated node may be determined according to a learning rate. The neural network's computation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently according to the number of iterations of the learning cycle of the neural network. For example, a high learning rate may be used in the early stage of neural network training to increase efficiency by allowing the neural network to quickly obtain a certain level of performance, and a low learning rate may be used in the late stage to increase accuracy.

In neural network learning, generally, training data can be a subset of real data (ie, data to be processed using the trained neural network), and therefore, errors for training data are reduced, but errors for real data are reduced. There may be incremental learning cycles. Overfitting is a phenomenon in which errors for actual data increase due to excessive learning on training data. For example, a phenomenon in which a neural network that has learned a cat by showing a yellow cat does not recognize that it is a cat when it sees a cat other than yellow may be a type of overfitting. Overfitting can act as a cause of increasing the error of machine learning algorithms. Various optimization methods can be used to prevent such overfitting. To prevent overfitting, methods such as increasing the training data, regularization, inactivating some nodes in the network during learning, and using a batch normalization layer should be applied. can

According to an embodiment of the present disclosure, a computer readable medium storing a data structure is disclosed.

Data structure can refer to the organization, management, and storage of data that enables efficient access and modification of data. Data structure may refer to the organization of data to solve a specific problem (eg, data retrieval, data storage, data modification in the shortest time). A data structure may be defined as a physical or logical relationship between data elements designed to support a specific data processing function. A logical relationship between data elements may include a connection relationship between user-defined data elements. A physical relationship between data elements may include an actual relationship between data elements physically stored in a computer-readable storage medium (eg, a persistent storage device). The data structure may specifically include a set of data, a relationship between data, and a function or command applicable to the data. Through an effectively designed data structure, a computing device can perform calculations while using minimal resources of the computing device. Specifically, the computing device can increase the efficiency of operation, reading, insertion, deletion, comparison, exchange, and search through an effectively designed data structure.

The data structure can be divided into a linear data structure and a non-linear data structure according to the shape of the data structure. A linear data structure may be a structure in which only one data is connected after one data. Linear data structures may include lists, stacks, queues, and decks. A list may refer to a series of data sets in which order exists internally. The list may include a linked list. A linked list may be a data structure in which data are connected in such a way that each data is connected in a single line with a pointer. In a linked list, a pointer can contain information about connection to the next or previous data. A linked list can be expressed as a singly linked list, a doubly linked list, or a circular linked list depending on the form. A stack can be a data enumeration structure that allows limited access to data. A stack can be a linear data structure in which data can be processed (eg, inserted or deleted) at only one end of the data structure. The data stored in the stack may be a LIFO-Last in First Out (Last in First Out) data structure. A queue is a data listing structure that allows limited access to data, and unlike a stack, it can be a data structure (FIFO-First in First Out) in which data stored later comes out later. A deck can be a data structure that can handle data from either end of the data structure.

The nonlinear data structure may be a structure in which a plurality of data are connected after one data. The non-linear data structure may include a graph data structure. A graph data structure can be defined as a vertex and an edge, and an edge can include a line connecting two different vertices. A graph data structure may include a tree data structure. The tree data structure may be a data structure in which one path connects two different vertices among a plurality of vertices included in the tree. That is, it may be a data structure that does not form a loop in a graph data structure.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. Hereinafter, a neural network is unified and described. The data structure may include a neural network. And the data structure including the neural network may be stored in a computer readable medium. The data structure including the neural network may also include preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation function associated with each node or layer of the neural network, and neural network It may include a loss function for learning of . A data structure including a neural network may include any of the components described above. That is, the data structure including the neural network includes preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation function associated with each node or layer of the neural network, and neural network. It may be configured to include all or any combination thereof, such as a loss function for learning of . In addition to the foregoing configurations, the data structure comprising the neural network may include any other information that determines the characteristics of the neural network. In addition, the data structure may include all types of data used or generated in the computational process of the neural network, but is not limited to the above. A computer readable medium may include a computer readable recording medium and/or a computer readable transmission medium. A neural network may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network includes one or more nodes.

The data structure may include data input to the neural network. A data structure including data input to the neural network may be stored in a computer readable medium. Data input to the neural network may include training data input during a neural network learning process and/or input data input to a neural network that has been trained. Data input to the neural network may include pre-processed data and/or data subject to pre-processing. Pre-processing may include a data processing process for inputting data to a neural network. Accordingly, the data structure may include data subject to pre-processing and data generated by pre-processing. The foregoing data structure is only an example, and the present disclosure is not limited thereto.

The data structure may include the weights of the neural network. (In this specification, weights and parameters may be used in the same meaning.) Also, a data structure including weights of a neural network may be stored in a computer readable medium. A neural network may include a plurality of weights. The weight may be variable, and may be changed by a user or an algorithm in order to perform a function desired by the neural network. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. A data value output from an output node may be determined based on the weight. The foregoing data structure is only an example, and the present disclosure is not limited thereto.

As a non-limiting example, the weights may include weights that are varied during neural network training and/or weights for which neural network training has been completed. The variable weight in the neural network learning process may include a weight at the time the learning cycle starts and/or a variable weight during the learning cycle. The weights for which neural network learning has been completed may include weights for which learning cycles have been completed. Accordingly, the data structure including the weights of the neural network may include a data structure including weights that are variable during the neural network learning process and/or weights for which neural network learning is completed. Therefore, it is assumed that the above-described weights and/or combinations of weights are included in the data structure including the weights of the neural network. The foregoing data structure is only an example, and the present disclosure is not limited thereto.

The data structure including the weights of the neural network may be stored in a computer readable storage medium (eg, a memory or a hard disk) after going through a serialization process. Serialization can be the process of converting a data structure into a form that can be stored on the same or another computing device and later reconstructed and used. A computing device may serialize data structures to transmit and receive data over a network. The data structure including the weights of the serialized neural network may be reconstructed on the same computing device or another computing device through deserialization. The data structure including the weights of the neural network is not limited to serialization. Furthermore, the data structure including the weights of the neural network is a data structure for increasing the efficiency of operation while minimizing the resource of the computing device (for example, B-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree). The foregoing is only an example, and the present disclosure is not limited thereto.

The data structure may include hyper-parameters of the neural network. Also, the data structure including the hyperparameters of the neural network may be stored in a computer readable medium. A hyperparameter may be a variable variable by a user. Hyperparameters include, for example, learning rate, cost function, number of learning cycle iterations, weight initialization (eg, setting the range of weight values to be targeted for weight initialization), hidden unit number (eg, the number of hidden layers and the number of nodes in the hidden layer). The foregoing data structure is only an example, and the present disclosure is not limited thereto.

3 is a diagram illustrating a method for predicting preferences performed in a computing device according to some embodiments of the present disclosure.

Referring to FIG. 3 , the processor 110 may input new game log data to a preference prediction model and generate result data related to the preference (S110).

The processor 110 may generate training data to train the preference prediction model. Specifically, the processor 110 may assign at least one game attribute to each of a plurality of games. The processor 110 may tag at least one game corresponding to at least one game attribute. When there are a plurality of game properties, the processor 110 may tag at least one game corresponding to each of the plurality of game properties. Accordingly, at least one game attribute may be assigned to each of the plurality of games, and at least one game corresponding to each of the plurality of game attributes may be tagged.

For example, when a new game is added, the processor 110 may assign at least one game property corresponding to the new game, and the new game may be tagged to the at least one game property. For example, if the processor 110 determines that the added first game is related to a puzzle, the processor 110 may assign 'puzzle game' as a game property to the first game. In addition, the first game may be tagged as 'puzzle game', which is a game property. Therefore, even if a new game is added, the processor 110 can reduce data voids and reduce the amount of calculations during data processing by tagging the new game to existing game properties without adding a column for the new game.

The processor 110 may calculate sub similarity values through a collaborative filtering algorithm based on the sampled first game log data. For example, the processor 110 may generate a similarity matrix including sub-similarity values that are similar degrees between a plurality of game attributes based on the sampled first game log data.

Accordingly, since the processor 110 calculates all sub-similarity values through a collaborative filtering algorithm based on the sampled first game log data, errors caused by data gaps can be reduced.

The processor 110 may calculate a plurality of sub-preference values for each of a plurality of game attributes of a plurality of users based on similarity values including the plurality of calculated sub-similarity values and information on connection of a plurality of games. there is. Accordingly, the processor 110 may calculate sub-preference values for each of all game attributes of all users included in the pre-stored game log data.

The processor 110 may generate learning data based on pre-stored game log data and preference values including sub-preference values. For example, the processor 110 may generate learning data by labeling preference values to pre-stored game log data.

The processor 110 may train a preference prediction model using the generated training data.

The processor 110 may determine whether the user has a preference for a specific game included in the new game log data based on the result data related to the preference (S120).

For example, the processor 110 may determine whether the first user prefers the first game. The processor 110 may also determine whether a user prefers a game for which there is no access record.

Specifically, the processor 110 may recognize at least one game property corresponding to a specific game. For example, the processor 110 may recognize 'puzzle game' as at least one game property corresponding to the first game.

The processor 110 may obtain a plurality of sub preference values corresponding to at least one recognized game attribute among the user's preference values included in the new game log data. For example, when the recognized game attribute is 'puzzle game', the processor 110 may obtain the first user's sub-preference value included in new game log data for 'puzzle game'.

The processor 110 may determine whether the user included in the new game log data has a preference for a specific game based on the user's sub-preference value included in the new game log data. For example, if the sub-preference value of the user included in the new game log data is greater than a predetermined threshold value (eg, 50 points), the processor 110 may assign the user included in the new game log data to a specific game. can be judged as a preference for For another example, the processor 110 determines that the user included in the new game log data is assigned to a specific game when the user's sub-preference value included in the new game log data is smaller than a predetermined threshold value (eg, 50 points). It can be judged that there is no preference for .

When there are a plurality of sub-preference values of the user included in the new game log data, the processor 110 compares the sum of the plurality of sub-preference values with a preset threshold value to determine whether a specific user prefers a specific game. can judge However, the method of determining future access is not limited to this, and the processor 110 compares various values such as the average and maximum values of a plurality of sub-preference values with a predetermined threshold value, and the new game log data included in the game log data. It is possible to determine whether the user has a preference for a specific game.

Meanwhile, according to some embodiments of the present disclosure, the processor 110 inputs result data related to preference and information on game properties of a specific game to a classification model to assign a user's specific game included in the new game log data. preference can be judged.

The classification model may calculate a final preference value based on the result data and attributes of a particular game. For example, the classification model may calculate a final preference value by adding up sub-preference values corresponding to properties of a specific game.

Further, the classification model may determine whether the user has a preference for a specific game included in the new game log data based on a final preference value based on a predetermined threshold value.

The steps shown in FIG. 3 are exemplary steps. Accordingly, it will also be apparent to those skilled in the art that some of the steps in FIG. 3 may be omitted or additional steps may be present without departing from the scope of the present disclosure. In addition, specific details about the configurations (eg, the computing device 100) described in FIG. 3 may be replaced with the contents described above with reference to FIGS. 1 and 2 .

4 is a diagram illustrating a method of calculating a plurality of sub-preference values performed by a computing device according to some embodiments of the present disclosure.

Referring to FIG. 4 , the processor 110 of the computing device 100 may create a tabular user table 10 including information about users, game properties, and the number of access days based on pre-stored game log data. there is.

Users in the user table 10 may be users included in pre-stored game log data. For example, in the user table 10, users may include 'user A' and 'user B'.

Attributes in the user table 10 may be game attributes. For example, attributes in the user table 10 may include game categories, social functions, and the like.

In the user table 10, the number of days of access may be the number of days of access to at least one game corresponding to the game property of the user. For example, in the user table 10, the number of access days for games corresponding to 'category 1' of 'user A' is '35', and the number of access days for games corresponding to 'category 2' for 'user A' is '18'. 'It can be.

The processor 110 of the computing device 100 may generate the tabular similarity table 20 including sub-similarity values calculated through a collaborative filtering algorithm based on the sampled first game log data.

In the similarity table 20, the reference attribute may be a game attribute that is a reference for calculating sub-similarity values. Accordingly, the processor 110 of the computing device 100 may select all game properties as standard properties.

Attributes in the similarity table 20 may be game attributes. For example, attributes in the similarity table 20 may include game categories, social functions, and the like.

The similarity in the similarity table 20 may include sub-similarity values. For example, in the similarity table 20, the similarity may include '0.5', which is a sub-similarity value of 'Category 2' to 'Category 1', and '0.4', which is a sub-similarity value of 'Category 3' to 'Category 1'. can

The processor 110 of the computing device 100 generates a tabular preference table 30 including sub-preference values for each of game attributes of users based on the user table 10 and the similarity table 20. can For example, the computing device 100 may generate the preference table 30 by combining and aggregating the user table 10 and the similarity table 20 .

Specifically, the computing device 100 may perform a join between the user table 10 and the similarity table 20 . A join may mean generating one result using two tables.

For example, the computing device 100 may perform an inner join between the attribute of the user table 10 and the reference attribute of the similarity table 20 to calculate a final degree of similarity for each of the game attributes of the user. An inner join can mean combining two tables based on a column specified in each of the two tables to create a single table. The final similarity is based on a predetermined condition (eg, maximum value, average value, etc.) among a plurality of sub-similarity values for each of the game attributes of the user generated through an inner join between the user table 10 and the similarity table 20. may be one similarity value selected based on For example, the final similarity is a plurality of sub-similarity values '1', '0.3', It may be '1', which is the maximum value among '0.2'. According to some other embodiments of the present disclosure, the final degree of similarity may be determined as 1 when the number of days the user accesses the corresponding game property is 1 or more (ie, when the user has experience playing a game corresponding to the corresponding game property). . Further, the final similarity may be determined as the maximum value among a plurality of sub-similarity values when the number of access days of the user to the corresponding game property is 0 (ie, when the user has no experience of playing a game corresponding to the corresponding game property).

The computing device 100 calculates a preference based on the final similarity determined after performing a join between the user table 10 and the similarity table 20 and the number of access days, and generates the preference table 30. can Here, the preference may be the root value of a value obtained by multiplying the final similarity by the number of access days. However, the preference is not limited thereto, and may be calculated through various methods based on the final similarity and the number of access days.

According to some embodiments of the present disclosure, the computing device 100 may determine a preference based on at least one of gender or age group of the new user in the case of a new user who has not had any connection days. For example, when the gender of the new user is male, the computing device 100 may determine the average preference value of all males as the preference of the new user.

Users in the preference table 30 may be users included in pre-stored game log data. For example, in the preference table 30, users may include 'user A' and 'user B'.

Attributes in the preference table 30 may be game attributes. For example, attributes in the preference table 30 may include game categories, social functions, and the like.

Preferences in the preference table 30 may include a plurality of sub-preference values for each of a plurality of game attributes of a plurality of users. For example, in the preference table 30, the preferences are '32.1', a sub-preference value corresponding to 'category 1' of 'user A', '18.2', a sub-preference value corresponding to 'category 2' of 'user A', and the like. can include

As described above with reference to FIG. 4 , the computing device 100 may calculate a plurality of sub preference values based on information of a plurality of users and similarity values of a plurality of game attributes. Accordingly, the computing device 100 may determine game preferences of a plurality of users through the calculated sub-preference values.

5 is a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has been described above as being generally embodied by a computing device, those skilled in the art will understand that the present disclosure may be combined with computer-executable instructions and/or other program modules that may be executed on one or more computers and/or may be implemented in hardware and software. It will be appreciated that it can be implemented as a combination.

Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will understand that the methods of the present disclosure can be applied to single-processor or multiprocessor computer systems, minicomputers, mainframe computers as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like ( It will be appreciated that each of these may be implemented with other computer system configurations, including those that may be operative in connection with one or more associated devices.

The described embodiments of the present disclosure may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer readable media. Computer readable media can be any medium that can be accessed by a computer, including volatile and nonvolatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media. By way of example, and not limitation, computer readable media may include computer readable storage media and computer readable transmission media. Computer readable storage media are volatile and nonvolatile media, transitory and non-transitory, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. includes media Computer readable storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device or other magnetic storage device. device, or any other medium that can be accessed by a computer and used to store desired information.

A computer readable transmission medium typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism. Including all information delivery media. The term modulated data signal means a signal that has one or more of its characteristics set or changed so as to encode information within the signal. By way of example, and not limitation, computer readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer readable transmission media.

An exemplary environment 1100 implementing various aspects of the present disclosure is shown including a computer 1102, which includes a processing unit 1104, a system memory 1106, and a system bus 1108. do. System bus 1108 couples system components, including but not limited to system memory 1106 , to processing unit 1104 . Processing unit 1104 may be any of a variety of commercially available processors. Dual processor and other multiprocessor architectures may also be used as the processing unit 1104.

System bus 1108 may be any of several types of bus structures that may additionally be interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112 . A basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, or EEPROM, and is a basic set of information that helps transfer information between components within computer 1102, such as during startup. contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

The computer 1102 may also include an internal hard disk drive (HDD) 1114 (eg, EIDE, SATA) - the internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes—a magnetic floppy disk drive (FDD) 1116 (e.g., for reading from or writing to a removable diskette 1118), and an optical disk drive 1120 (e.g., a CD-ROM) for reading disc 1122 or reading from or writing to other high capacity optical media such as DVDs). The hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 are connected to the system bus 1108 by a hard disk drive interface 1124, magnetic disk drive interface 1126, and optical drive interface 1128, respectively. ) can be connected to The interface 1124 for external drive implementation includes at least one or both of USB (Universal Serial Bus) and IEEE 1394 interface technologies.

These drives and their associated computer readable media provide non-volatile storage of data, data structures, computer executable instructions, and the like. In the case of computer 1102, drives and media correspond to storing any data in a suitable digital format. Although the description of computer readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art can use zip drives, magnetic cassettes, flash memory cards, cartridges, etc. It will be appreciated that other tangible media readable by the computer, such as the like, may also be used in the exemplary operating environment and any such media may include computer executable instructions for performing the methods of the present disclosure.

A number of program modules may be stored on the drive and RAM 1112, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or portions of the operating system, applications, modules and/or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented in a variety of commercially available operating systems or combinations of operating systems.

A user may enter commands and information into the computer 1102 through one or more wired/wireless input devices, such as a keyboard 1138 and a pointing device such as a mouse 1140. Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. Although these and other input devices are often connected to the processing unit 1104 through an input device interface 1142 that is connected to the system bus 1108, a parallel port, IEEE 1394 serial port, game port, USB port, IR interface, may be connected by other interfaces such as the like.

A monitor 1144 or other type of display device is also connected to the system bus 1108 through an interface such as a video adapter 1146. In addition to the monitor 1144, computers typically include other peripheral output devices (not shown) such as speakers, printers, and the like.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148 via wired and/or wireless communications. Remote computer(s) 1148 may be a workstation, computing device computer, router, personal computer, handheld computer, microprocessor-based entertainment device, peer device, or other common network node, and generally includes It includes many or all of the components described for, but for simplicity, only memory storage device 1150 is shown. The logical connections shown include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, such as a wide area network (WAN) 1154 . Such LAN and WAN networking environments are common in offices and corporations and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to worldwide computer networks, such as the Internet.

When used in a LAN networking environment, computer 1102 connects to local network 1152 through wired and/or wireless communication network interfaces or adapters 1156. Adapter 1156 may facilitate wired or wireless communications to LAN 1152, which also includes a wireless access point installed therein to communicate with wireless adapter 1156. When used in a WAN networking environment, computer 1102 may include a modem 1158, be connected to a communicating computing device on WAN 1154, or establish communications over WAN 1154, such as over the Internet. have other means. A modem 1158, which may be internal or external and a wired or wireless device, is connected to the system bus 1108 through a serial port interface 1142. In a networked environment, program modules described for computer 1102, or portions thereof, may be stored on remote memory/storage device 1150. It will be appreciated that the network connections shown are exemplary and other means of establishing a communication link between computers may be used.

Computer 1102 is any wireless device or entity that is deployed and operating in wireless communication, eg, printers, scanners, desktop and/or portable computers, portable data assistants (PDAs), communication satellites, wireless detectable tags associated with It operates to communicate with arbitrary equipment or places and telephones. This includes at least Wi-Fi and Bluetooth wireless technologies. Thus, the communication may be a predefined structure as in conventional networks or simply an ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) makes it possible to connect to the Internet without wires. Wi-Fi is a wireless technology, such as a cell phone, that allows such devices, eg, computers, to transmit and receive data both indoors and outdoors, i.e. anywhere within coverage of a base station. Wi-Fi networks use a radio technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz radio bands, for example, at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or in products that include both bands (dual band) .

Those skilled in the art will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, instructions, information, signals, bits, symbols and chips that may be referenced in the above description are voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields s or particles, or any combination thereof.

Those skilled in the art will understand that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein are electronic hardware, (for convenience) , may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and the design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

Various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical disks (eg, CDs, DVDs, etc.), smart cards, and flash memory devices (eg, EEPROM, cards, sticks, key drives, etc.), but are not limited thereto. Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of example approaches. Based upon design priorities, it is to be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of this disclosure. The accompanying method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art of this disclosure, and the general principles defined herein may be applied to other embodiments without departing from the scope of this disclosure. Thus, the present disclosure is not to be limited to the embodiments presented herein, but is to be interpreted in the widest scope consistent with the principles and novel features presented herein.

Claims (12)

A method for predicting preferences performed by a computing device comprising one or more processors, the method comprising:
generating result data related to preference by inputting new game log data into a preference prediction model;
including,
The preference prediction model,
Learned using learning data labeled with preference values in pre-stored game log data.
A method for predicting preferences.
According to claim 1,
The pre-stored game log data,
Including information about a plurality of games, information about a plurality of game properties, information about a plurality of users, and information about access to the plurality of games,
A method for predicting preferences.
According to claim 2,
The plurality of game properties,
Determined based on at least one of the game category, intellectual property (IP), progression method, graphics, business model, social function, or genre of the game world,
A method for predicting preferences.
According to claim 2,
At least one game property is given to each of the plurality of games, and at least one game corresponding to each of the plurality of game properties is tagged.
A method for predicting preferences.
According to claim 1,
The preference value is,
Including a plurality of sub-preference values for each of a plurality of game attributes of a plurality of users,
A method for predicting preferences.
According to claim 5,
The plurality of sub preference values,
Calculated based on information about access of a plurality of games and similarity values of the plurality of game properties,
A method for predicting preferences.
According to claim 6,
The similarity value is,
Including sub-similarity values that are similar degrees between the plurality of game attributes,
A method for predicting preferences.
According to claim 7,
The sub-similarity values are
Calculated through a collaborative filtering algorithm based on at least one first game log data among the pre-stored game log data,
A method for predicting preferences.
According to claim 8,
The at least one first game log data,
Sampled based on information about access of the plurality of games,
A method for predicting preferences.
According to claim 1,
determining whether a user has a preference for a specific game included in the new game log data based on result data related to the preference;
Including more,
A method for predicting preferences.
A computer program stored on a computer readable storage medium, the computer program including instructions for causing a processor of a computing device for predicting a preference to perform the following steps:
generating result data related to preference by inputting new game log data into a preference prediction model;
including,
The preference prediction model,
Learned using learning data labeled with preference values in pre-stored game log data.
A computer program stored on a computer readable storage medium.
A computing device for predicting preferences,
processor;
a memory storing a computer program executable by the processor; and
network unit;
including,
By inputting new game log data into the preference prediction model, result data related to preference is generated,
The preference prediction model,
Learned using learning data labeled with preference values in pre-stored game log data.
computing device.

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