Electronic device and control method therefor

Abstract

This electronic device comprises: a memory for storing at least one instruction; and at least one processor operably coupled to the memory. When executed individually or collectively by the at least one processor, the instructions instruct the electronic device to: acquire first data corresponding to a first user access history for a plurality of contents classified according to a plurality of types; acquire second data corresponding to a plurality of attributes for the respective types; acquire first score information, corresponding to a priority of the respective contents for the first user, on the basis of first probability information corresponding to the influence of each of the plurality of attributes on the first user, the first probability information being acquired by inputting the first data and the second data into a neural network model; and provide at least one piece of recommended content for the first user on a display on the basis of the first score information.

Description

Electronic device and control method thereof

[0001] The present disclosure relates to an electronic device and a method for controlling an electronic device, and specifically to an electronic device capable of providing recommended content to a user and a method for controlling the same.

[0002] Recently, technology utilizing artificial intelligence to recommend content suitable for individual users (e.g., personalized) from a vast amount of content has been advancing. For example, there has been a continuous development in technologies that provide personalized content by accurately analyzing users' tastes and interests using deep learning and reinforcement learning algorithms, technologies that recommend content by combining various data sources, and technologies that provide explanations for the reasons behind content recommendations.

[0003] In particular, according to the related technology, an artificial intelligence model can utilize a user's content access history to identify attributes preferred by the user (e.g., genre, actor, etc.) and recommend content with the identified attributes to the user.

[0004] However, since the related technology does not consider how each of the content attributes influences the user's content selection, there may be limitations in recommending content by accurately analyzing users' tastes and interests, as well as limitations in terms of the diversity of content recommendations.

[0005] Furthermore, according to the relevant technology, while it is possible to recommend content by considering the priority among content of the same type, it has limitations in that it is difficult to recommend content suitable for the user by considering the priority among different types of content (e.g., video content and music content).

[0006] The present disclosure is intended to solve the problems of the related technology described above, and the purpose of the present disclosure is to provide recommended content suitable for the user by considering the impact of the attributes of various content on the user.

[0007] According to one aspect of the present disclosure, an electronic device comprises a memory for storing at least one instruction and at least one processor coupled to the memory so as to be operable thereto, wherein when the instructions are executed individually or collectively by the at least one processor, the electronic device acquires first data corresponding to the access history of a first user for a plurality of contents classified according to a plurality of types, acquires second data corresponding to a plurality of attributes for each of the plurality of types, acquires first score information corresponding to the priority of each of the plurality of contents for the first user based on first probability information corresponding to the influence of each of the plurality of attributes on the first user obtained by inputting the first data and the second data into a neural network model, and provides at least one recommended content for the first user through a display based on the first score information.

[0008] When the above instructions are executed individually or collectively by the at least one processor, the electronic device may provide the at least one recommended content so that the at least one recommended content is displayed in order according to the priority in a user interface provided within the display based on the first score information.

[0009] When the above instructions are executed individually or collectively by the at least one processor, the electronic device may acquire the first score information based on the priority of the first user for each of the plurality of contents, without considering the priority of the first user for each of the plurality of types.

[0010] The first probability information includes first probability values ​​representing the influence of each of the plurality of attributes on the first user and second probability values ​​representing the influence of each of the plurality of detailed attributes distinguishing each of the plurality of attributes on the first user, and when the instructions are executed individually or collectively by the at least one processor, the electronic device may obtain the first probability values ​​based on the second probability values ​​for each of the plurality of attributes.

[0011] The above neural network model can obtain the first probability information based on increasing the probability value of at least one attribute corresponding to the first content among the plurality of attributes when the first user accesses the first content among the plurality of contents.

[0012] The above neural network model can obtain the first probability information based on increasing the probability value for the at least one attribute corresponding to the first content if the first content is a recommendation content provided based on one of the plurality of attributes.

[0013] The above neural network model can obtain the first probability information by assigning weights to the at least one attribute corresponding to the first content based on time information included in the first data and indicating the time of access.

[0014] When the above instructions are executed individually or collectively by the at least one processor, the electronic device may identify a second probability information among the probability information for each of the plurality of users, the similarity with the first probability information is greater than or equal to a threshold value, identify a second user among the plurality of users corresponding to the second probability information, and provide the at least one recommended content for the first user based on third data corresponding to the access history of the second user to the plurality of content.

[0015] When the above instructions are executed individually or collectively by the at least one processor, the electronic device may provide, based on the third data, a type of content among the plurality of types that is not included in the access history of the first user as one of the at least one recommended content.

[0016] The electronic device further includes a communication circuit, and when the instructions are executed individually or collectively by the at least one processor, the electronic device controls the communication circuit to transmit information about the at least one recommendation content to the user terminal of the user, and when information about user feedback is received from the user terminal through the communication circuit, the neural network model may be trained based on the information about the user feedback.

[0017] The electronic device further includes the display, and when the instructions are executed individually or collectively by the at least one processor, the electronic device controls the display to display a user interface including a plurality of objects corresponding to the at least one recommended content based on the first score information, and the plurality of objects may be arranged in order according to the priority within the user interface.

[0018] According to one aspect of the present disclosure, a control method for an electronic device comprises the steps of: acquiring first data corresponding to the access history of a first user for a plurality of contents classified according to a plurality of types; acquiring second data corresponding to a plurality of attributes for each of the plurality of types; acquiring first score information corresponding to the priority of each of the plurality of contents for the first user based on first probability information corresponding to the influence of each of the plurality of attributes on the first user obtained by inputting the first data and the second data into a neural network model; and providing at least one recommended content for the first user through a display based on the first score information.

[0019] The step of providing the recommended content may include the step of providing the at least one recommended content in order according to the priority in a user interface provided within the display, based on the first score information.

[0020] The step of obtaining the first score information may be to obtain the first score information based on the priority of the first user for each of the plurality of contents, without considering the priority of the first user for each of the plurality of types.

[0021] The first probability information includes first probability values ​​representing the influence of each of the plurality of attributes on the first user and second probability values ​​representing the influence of each of the plurality of detailed attributes distinguishing each of the plurality of attributes on the first user, and the step of obtaining the first probability information may include the step of obtaining the first probability values ​​based on the second probability values ​​for each of the plurality of attributes.

[0022] The above and other aspects, features, and advantages of specific embodiments of the present disclosure will become more apparent when referred to together with the accompanying drawings.

[0023] FIG. 1 is a block diagram briefly illustrating the configuration of an electronic device according to one or more embodiments,

[0024] FIG. 2 is a drawing for explaining a neural network model and a plurality of modules according to one or more embodiments,

[0025] FIG. 3 is a drawing for explaining multiple types and multiple attribute-specific probabilities according to one or more embodiments,

[0026] FIG. 4 is a drawing for explaining a plurality of content-specific scores according to one or more embodiments,

[0027] FIG. 5 is a diagram illustrating a method for providing recommended content based on the access history of other users with similar probability information according to one or more embodiments,

[0028] FIG. 6 is a drawing for illustrating a user interface according to one or more embodiments,

[0029] FIG. 7 is a block diagram showing in detail the configuration of an electronic device according to one or more embodiments, and,

[0030] FIG. 8 is a flowchart illustrating a method for controlling an electronic device according to one or more embodiments.

[0031] The embodiments described herein are subject to various modifications and may have various forms; specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the scope of specific embodiments and should be understood to include various modifications, equivalents, and / or alternatives of the embodiments of the present disclosure. In relation to the description of the drawings, similar reference numerals may be used for similar components.

[0032] In describing the present disclosure, if it is determined that a detailed description of related known functions or configurations could unnecessarily obscure the essence of the present disclosure, such detailed description is omitted.

[0033] Additionally, the following embodiments may be modified in various other forms, and the scope of the technical concept of the present disclosure is not limited to the following embodiments. Rather, these embodiments are provided to make the present disclosure more faithful and complete and to fully convey the technical concept of the present disclosure to those skilled in the art.

[0034] The terms used in this disclosure are used merely to describe specific embodiments and are not intended to limit the scope of the rights. The singular expression includes the plural expression unless the context clearly indicates otherwise.

[0035] In the present disclosure, expressions such as “have,” “may have,” “include,” or “may include” indicate the presence of such features (e.g., numerical values, functions, actions, or components such as parts) and do not exclude the presence of additional features.

[0036] In the present disclosure, expressions such as “A or B,” “at least one of A or / and B,” or “one or more of A or / and B” may include all possible combinations of items listed together. For example, the expression “at least one A or B” should be understood as including only A, only B, or both A and B.

[0037] Expressions such as "first," "second," "first," or "second" used in this disclosure may modify various components regardless of order and / or importance, and are used only to distinguish one component from another and do not limit said components.

[0038] Where it is stated that a certain component (e.g., a first component) is "(operatively or communicatively) coupled with / to" or "connected to" another component (e.g., a second component), it should be understood that the said certain component may be directly connected to the said other component or connected through another component (e.g., a third component).

[0039] On the other hand, when it is stated that a certain component (e.g., a first component) is "directly connected" or "directly coupled" to another component (e.g., a second component), it may be understood that no other component (e.g., a third component) exists between said certain component and said other component.

[0040] As used in this disclosure, the expression “configured to” may be replaced, depending on the context, with, for example, “suitable for,” “having the capacity to,” “designed to,” “adapted to,” “made to,” or “capable of.” The term “configured to” may not necessarily mean only “specifically designed to” in hardware.

[0041] Instead, in some situations, the expression “device configured to do something” may mean that the device is “capable of doing something” together with other devices or components. For example, the phrase “processor configured (or set) to perform A, B, and C” may mean a dedicated processor for performing those operations (e.g., an embedded processor), or a generic-purpose processor (e.g., a CPU or application processor) capable of performing those operations by executing one or more software programs stored in a memory device.

[0042] In the embodiments, a 'module' or 'part' performs at least one function or operation and may be implemented in hardware or software, or a combination of hardware and software. Additionally, a plurality of 'modules' or a plurality of 'parts' may be integrated into at least one module and implemented by at least one processor, except for the 'module' or 'part' that needs to be implemented in specific hardware.

[0043] Meanwhile, the various elements and areas in the drawings are depicted schematically. Accordingly, the technical concept of the present invention is not limited by the relative sizes or spacing depicted in the attached drawings.

[0044] Hereinafter, embodiments according to the present disclosure are described in detail with reference to the attached drawings so that those skilled in the art can easily implement them.

[0045] FIG. 1 is a block diagram briefly illustrating the configuration of an electronic device (100) according to one or more embodiments. FIG. 2 is a diagram for explaining a neural network model (1010) and a plurality of modules according to one or more embodiments. Hereinafter, various embodiments will be described with reference to FIG. 1 and FIG. 2 together.

[0046] As illustrated in FIG. 1, the electronic device (100) may include a memory (110) and at least one processor (120). However, the configurations illustrated in FIG. 1 are examples, and a more detailed configuration of the electronic device (100) will be described later with reference to FIG. 7.

[0047] The electronic device (100) according to the present disclosure refers to a device capable of providing recommended content to a user. In one or more embodiments, the electronic device (100) may provide recommended content suitable for the user by using a neural network model (1010).

[0048] For example, the electronic device (100) may be implemented as a server, in which case the electronic device (100) may obtain information about recommended content using a neural network model (1010) included in the electronic device (100) and transmit information about recommended content to a user terminal. As another example, the electronic device (100) may be implemented as various devices such as a smartphone, a digital television, etc., in which case the electronic device (100) may include a neural network model (1010) on-device. There are no particular limitations on the type of electronic device (100) according to the present disclosure.

[0049] At least one instruction regarding an electronic device (100) may be stored in the memory (110). Additionally, an operating system (O / S) for operating the electronic device (100) may be stored in the memory (110). Furthermore, various software programs or applications for operating the electronic device (100) may be stored in the memory (110) according to various embodiments of the present disclosure. Additionally, the memory (110) may include semiconductor memory such as flash memory or magnetic storage media such as a hard disk.

[0050] Specifically, various software modules for operating an electronic device (100) according to various embodiments of the present disclosure may be stored in the memory (110), and a processor (120) may control the operation of the electronic device (100) by executing the various software modules stored in the memory (110). For example, the memory (110) may be accessed by the processor (120), and reading / writing / modifying / deleting / updating of data by the processor (120) may be performed.

[0051] In the present disclosure, the term memory (110) may be used to include memory (110), ROM, RAM, or a memory card (e.g., micro SD card, memory stick) mounted in the electronic device (100) within the processor (120).

[0052] In one or more embodiments, the memory (110) may store user data (e.g., first data and third data), second data (e.g., metadata), data regarding a neural network model (1010), probability information, score information, information regarding recommended content, etc. Additionally, the memory (110) may store information regarding a user interface. Furthermore, various information necessary within the scope of achieving the purpose of the present disclosure may be stored in the memory (110), and the information stored in the memory (110) may be updated as it is received from an external device or input by a user.

[0053] The processor (120) controls the overall operation of the electronic device (100). In one or more embodiments, the processor (120) is connected to a configuration of the electronic device (100) including a memory (110), and can control the overall operation of the electronic device (100) by executing at least one instruction stored in the memory (110) as described above.

[0054] The processor (120) can be implemented in various ways. For example, the processor (120) can be implemented as at least one of an Application Specific Integrated Circuit (ASIC), an embedded processor, a microprocessor, hardware control logic, a hardware Finite State Machine (FSM), and a Digital Signal Processor (DSP). In this disclosure, the term processor (120) may be used to include a Central Processing Unit (CPU), a Graphic Processing Unit (GPU), and a Micro Processor Unit (MPU).

[0055] In one or more embodiments, the processor (120) may provide recommended content to a user using a neural network model (1010). As illustrated in FIG. 2, the processor (120) may implement various embodiments according to the present disclosure using a neural network model (1010), a profiling module (1020), and a recommendation module (1030). Various embodiments implemented by the processor (120) will be described below.

[0056] The processor (120) can obtain first data corresponding to the access history of a first user for multiple contents classified according to multiple types, and second data corresponding to multiple attributes for each of the multiple types. In one or more embodiments, the first data and the second data may be obtained from the memory (110) of an electronic device. In one or more embodiments, at least one of the first data or the second data may be obtained from another electronic device or a remote server.

[0057] The first data and the second data may be acquired (or collected) and stored in memory (110) whenever the data changes or at preset intervals. Additionally, the processor (120) may acquire the first data and the second data by loading the first data and the second data stored in the memory (110) of the electronic device (100). The processor (120) may also acquire the first data and the second data by receiving the first data and the second data from an external device.

[0058] Multiple 'contents' may be classified according to multiple 'types,' and multiple types may be defined in various ways. For example, multiple types may include VOD (Video On Demand) content, broadcast content, music content, game content, and art content. As another example, multiple types may include video content, photo content, music content, etc. Furthermore, multiple types may be distinguished according to the type of service providing the content, the type of application providing the content, the content provider, etc. For example, the type of content may be distinguished depending on whether the content is provided by a streaming service or is broadcast content.

[0059] The user's access history regarding multiple contents may indicate that the user has never accessed them. That is, the multiple contents do not all refer to content that the first user has accessed, and the term "multiple contents" may refer to all content included in the database that the electronic device (100) can access.

[0060] "User data" may collectively refer to data corresponding to (or representing) a user's access history regarding multiple contents. Among these, the first data may specifically refer to data corresponding to the first user's access history regarding multiple contents. For example, user data may include information regarding the time, number of times, frequency, etc., of a user accessing specific content. User access may include viewing content, user input for selecting or searching for information regarding content (e.g., touch input, click, etc.).

[0061] "Second data" may collectively refer to data corresponding to (or representing) the attributes of multiple contents. Specifically, the second data may include metadata, and the metadata may represent multiple attributes for each of the multiple types. That is, since the attributes of the contents may differ depending on the type of multiple contents, the second data may be constructed under different criteria for each type of content. For example, if the content is VOD content, the multiple attributes included in the second data may include genre, director, and performer. If the content is music content, the multiple attributes included in the second data may include genre, composer / lyricist, and performer.

[0062] According to an embodiment, information regarding multiple 'detailed attributes' that distinguish each of the multiple attributes may be included in the second data. For example, if the content is VOD content and the multiple attributes include genre, director, and cast, the attribute of genre may be divided into various detailed attributes such as action, drama, horror, thriller, etc., and the attributes of director and cast may be divided into the names of various directors and the names of cast members.

[0063] The term 'type' can be replaced with terms such as domain, category, and class. The term 'attribute' can be replaced with terms such as 'feature' or 'item,' and the term 'detailed attribute' can be replaced with terms such as 'element' or 'sub-item.' Examples of types, attributes, and detailed attributes are explained in more detail with reference to Fig. 3.

[0064] The processor (120) can obtain first score information corresponding to the priority of each of the first users of each of the multiple contents based on first probability information corresponding to the influence of each of the multiple attributes on each of the first users obtained by inputting first data and second data into a neural network model.

[0065] Specifically, the processor (120) inputs the first data and the second data into a learned neural network model (1010) to obtain first probability information representing the influence of each of the multiple attributes on the first user.

[0066] The 'neural network model (1010)' refers to an artificial intelligence model trained to analyze the impact that each of the various attributes related to the content has on the user. As illustrated in FIG. 2, when user data and second data are input, the neural network model (1010) can output first probability information. Specifically, when the first data and second data are input, the neural network model (1010) can obtain first probability information based on whether the first user's access to multiple contents corresponds to which of the multiple attributes. For example, the neural network model (1010) may include neural networks such as CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory), FFNN (Feedforward Neural Networks), and Transformers, but is not limited thereto.

[0067] "Probability information (or probability, probability value, etc.)" may collectively refer to information regarding probabilities representing the influence of each of multiple attributes on a user. Among these, "first probability information" may specifically refer to probability information representing the influence of each of the multiple attributes on a first user. The probability information may include information indicating the user's preference for each of the multiple attributes. The probability information may be updated whenever user data or second data is updated, or it may be updated at preset intervals.

[0068] Probability information may include multiple probability values, and among the multiple probability values, the probability value of an attribute that has a significant influence on the user's selection of content may be high, and the probability value of an attribute that has a minor influence on the user's selection of content may be low. In one or more embodiments, since the probability values ​​corresponding to each of the multiple attributes represent the user's individual (or independent) preference for each of the multiple attributes, the sum of the probability values ​​corresponding to each of the multiple attributes does not have to be 1.

[0069] In one or more embodiments, the first probability information may include first probability values ​​representing the influence on a first user for each of a plurality of attributes and second probability values ​​representing the influence on a first user for each of a plurality of detailed attributes distinguishing each of the plurality of attributes. That is, the first probability information may include probability information for a plurality of attributes as well as probability information for a plurality of detailed attributes.

[0070] In this case, the processor (120) can obtain first probability values ​​based on multiple second probability values ​​for each attribute. Specifically, the processor (120) can obtain first probability values ​​by combining multiple second probability values ​​for each attribute. For example, combining second probability values ​​may mean summing the second probability values ​​or taking a weighted sum, but there are no limitations on the specific method of operation. Meanwhile, as described below, the first probability value may be obtained independently of the second probability value.

[0071] In one or more embodiments, when a first user accesses a first content among a plurality of contents, the neural network model (1010) can obtain first probability information based on increasing the probability value for at least one attribute corresponding to the first content among a plurality of attributes. That is, the neural network model (1010) can be trained to increase the probability value for the attribute corresponding to the content accessed by the user, and thus the neural network model (1010) can output a higher probability value for the attribute corresponding to the specific content as the number or frequency of user access to the specific content increases.

[0072] In one or more embodiments, when a first user accesses a first content among a plurality of contents, if the first content is a recommended content provided based on one of a plurality of attributes, the neural network model (1010) can obtain first probability information based on increasing the probability value for one attribute.

[0073] For example, if the first content is a recommendation content provided based on the attribute of supervision, the fact that a user accessed the first content may indicate that the user is influenced by the attribute of supervision. In particular, if information regarding recommendations based on supervision is provided together with the first content, the fact that a user accessed the first content may indicate that the user is greatly influenced by the attribute of supervision. Therefore, in this case, the neural network model (1010) can output a high probability value for the attribute of supervision.

[0074] In one or more embodiments, the neural network model (1010) can obtain first probability information by assigning weights to at least one attribute corresponding to the first content based on time information included in the first data and indicating the time of access.

[0075] For example, the first data may include time information regarding the time when the first user accessed the first content and time information regarding the time when the first user accessed the second content. In this case, if the time when the first user accessed the first content is more recent than the time when the second content was accessed, the processor (120) may give a higher weight to the attribute corresponding to the first content than to the attribute corresponding to the second content.

[0076] As another example, the processor (120) may obtain first probability information using only information about the history of the first user accessing the first content within a pre-set period (e.g., the last month).

[0077] The processor (120) can obtain first score information indicating the priority for each of the first users of the plurality of contents based on first probability information. The processor (120) can obtain the first score information using a profiling module (1020).

[0078] The 'profiling module (1020)' refers to a module that analyzes the influence of each of the multiple attributes on the user. As illustrated in FIG. 2, when probability information is input, the profiling module (1020) can obtain score information. The profiling module (1020) can be implemented as a neural network model trained to obtain score information when probability information is input. For example, the profiling module (1020) may be a second neural network model that receives the output of the neural network model (1010) as input. Accordingly, the configuration illustrated in FIG. 2 may include an interconnected network of neural network models.

[0079] "Score information (or score, score value, etc.)" may collectively refer to information indicating the priority for each user of multiple contents. Among these, "first score information" may specifically refer to information indicating the priority for each first user of multiple contents. Score information may be updated whenever probability information is updated, or it may be updated at preset intervals.

[0080] In one or more embodiments, the processor (120) can obtain first score information indicating the priority for each of the first users of the plurality of contents by assigning a higher score to the content containing more of the attributes / detailed attributes that the user considers more when selecting the content, based on at least one of the plurality of attribute-specific probability values ​​and the plurality of detailed attribute-specific probability values ​​included in the first probability information.

[0081] In one or more embodiments, the processor (120) may obtain first score information by using only the probability values ​​for multiple attributes, without using the probability values ​​for multiple detailed attributes included in the first probability information. Additionally, the processor (120) may obtain first score information by using the probability values ​​for multiple attributes together with the probability value corresponding to the detailed attribute with the highest probability value among the probability values ​​for multiple detailed attributes included in the first probability information.

[0082] The processor (120) can provide at least one recommended content for the first user based on the first score information. The processor (120) can obtain the first score information using the recommendation module (1030).

[0083] The ‘recommendation module (1030)’ refers to a module capable of analyzing recommended content to be provided to a user and obtaining information about the recommended content. As illustrated in FIG. 2, when score information is input, the recommendation module (1030) can output information about the recommended content. The recommendation module (1030) can be implemented as a neural network model trained to obtain information about the recommended content when score information is input.

[0084] The processor (120) can provide at least one recommended content by displaying at least one recommended content on a display included in the electronic device (100), and can provide at least one recommended content by transmitting information about at least one recommended content to an external device such as a user terminal. In particular, the processor (120) can provide at least one recommended content for a first user based on a plurality of content-specific priorities included in the first score information.

[0085] In one or more embodiments, the processor (120) can identify (or determine) at least one recommended content for the first user based on the first score information and can provide the identified recommended content. For example, the processor (120) can identify a preset number of high-priority content included in the first score information from a database of recommended content and provide the identified content as recommended content for the first user.

[0086] The processor (120) can not only identify recommended content according to the priority of score information, but also provide recommended content sequentially according to priority or control how recommended content is displayed.

[0087] In one or more embodiments, the processor (120) may provide at least one recommended content based on the first score information so that at least one recommended content is displayed in a user interface in a priority order. Here, the user interface may include not only that which can be provided by the electronic device (100) but also that which can be provided by an external device.

[0088] For example, if the score for the first content is 0.8 and the score for the second content is 0.6, the processor (120) may display the first content as recommended content within the user interface. If user input rejecting access to the first content through the user interface is received, the processor (120) may display the second content as recommended content within the user interface.

[0089] In the above example, the processor (120) may display the first content and the second content together within the user interface as recommended content, and may adjust the position of the first object corresponding to the first content to be above or to the left of the second object corresponding to the second content. In this case, the processor (120) may adjust the size of the first object corresponding to the first content to be larger than the second object corresponding to the second content. The user interface will be described in more detail with reference to FIG. 6.

[0090] According to the embodiments described above with reference to FIGS. 1 and 2, the electronic device (100) can provide recommended content suitable for the user by considering the influence of various content attributes / detailed attributes on the user. Accordingly, the electronic device (100) can recommend content by accurately analyzing the user's tastes and interests.

[0091] Additionally, the electronic device (100) can be used to recommend content by considering the impact that each of the detailed attributes, as well as the attributes of the content, has on the user, and can also recommend content to the user by considering the priority among different types of content.

[0092] FIG. 3 is a diagram for explaining multiple types and multiple attribute-specific probabilities according to one or more embodiments.

[0093] FIG. 3 is a table showing exemplary probability information. As described above, the first probability information may include first probability values ​​representing the influence on a first user for each of a plurality of attributes, and second probability values ​​representing the influence on a first user for each of a plurality of detailed attributes distinguishing each of the plurality of attributes. FIG. 3 exemplarily illustrates a plurality of types, a plurality of attributes, and a plurality of detailed attributes according to the present disclosure, and exemplarily illustrates a first probability value for each of the plurality of attributes and a second probability value for the plurality of detailed attributes.

[0094] As illustrated in FIG. 3, the plurality of types may include VOD content, game content, and music content. For convenience of explanation, the plurality of types described below will be limited to the three types of FIG. 3. Likewise, the plurality of attributes and the plurality of detailed attributes of FIG. 3 are merely illustrative for convenience of explanation.

[0095] Referring to FIG. 3, VOD content may include attributes such as VOD genre, director, and cast. The VOD genre may include detailed attributes such as action, drama, and horror, the director may include detailed attributes such as Jain and Tom, and the cast may include detailed attributes such as Jack and Bong.

[0096] Referring to FIG. 3, the game content may include attributes such as game genre and mode. The game genre may include detailed attributes such as action, racing, and sports, and the mode may include detailed attributes such as single and multi.

[0097] Referring to FIG. 3, the music content may include attributes such as music genre, creator, and performer. The music genre may include detailed attributes such as blues, jazz, and rock, the creator may include detailed attributes such as Robert and Pat, and the performer may include detailed attributes such as Eric and Jaco.

[0098] The numbers next to the attributes and detailed attributes in Fig. 3 represent the first probability value for each attribute and the second probability value for each detailed attribute. Specifically, the numbers next to the attributes and detailed attributes in Fig. 3 may indicate the influence of each attribute and each detailed attribute on the first user's content selection.

[0099] For example, information that VOD genre is 0.8 and information that director is 0.1 may indicate that the influence of VOD genre on the user (or the likelihood that the user will select content based on their preference for VOD genre) is 8 times greater than the influence of director on the user. Information that action is 0.8 and drama is 0.5 may indicate that the genre of action has an 8 / 5 times greater influence on the user's content selection than the genre of drama. Therefore, a higher weight may be assigned to the first attribute, which has a greater influence than the second attribute.

[0100] As mentioned above, since the probability values ​​corresponding to each of the multiple attributes represent the user's individual (or independent) preference for each of the multiple attributes, the sum of the probability values ​​corresponding to each of the multiple attributes does not have to be 1.

[0101] In one or more embodiments, the processor (120) may obtain first probability values ​​by combining second probability values ​​for a plurality of attributes. For example, "combining" the second probability values ​​may mean summing the second probability values ​​or taking a weighted sum. However, the embodiments are not limited to such specific calculation methods and may include any calculation method known to a person skilled in the art. Accordingly, the electronic device (100) can be used for content recommendation by considering the impact that each detailed attribute, as well as the attributes of the content, has on the user.

[0102] FIG. 4 is a drawing for explaining a plurality of content-specific scores according to one or more embodiments.

[0103] FIG. 4 is a table showing exemplary score information. As described above, when probability values ​​for multiple attributes and probability values ​​for multiple detailed attributes are obtained as in FIG. 3, the processor (120) can obtain first score information indicating the priority for each first user of each of the multiple contents based on at least one of the multiple probabilities for each attribute and multiple probabilities for each detailed attribute included in the first probability information. The processor (120) can assign a higher score to content that contains more attributes / detailed attributes that the user considers a lot when selecting content.

[0104] In one or more embodiments, the processor (120) may obtain first score information by using only the probability values ​​for multiple attributes, without using the probability values ​​for multiple detailed attributes included in the first probability information. Additionally, the processor (120) may obtain first score information by using the probability values ​​for multiple attributes together with the probability value corresponding to the detailed attribute with the highest probability value among the probability values ​​for multiple detailed attributes included in the first probability information.

[0105] As shown in FIG. 4, the score of VOD content 1 may be 0.8242, the score of VOD content 2 may be 0.6912, the score of VOD content 3 may be 0.7010, the score of game content 1 may be 0.6814, the score of game content 2 may be 0.7323, the score of music content 1 may be 0.7112, the score of broadcast content 1 may be 0.5322, and the score of art content 1 may be 0.4111. In this case, the processor (120) may provide recommended content in the order of VOD content 1, game content 2, music content 1, VOD content 3, VOD content 2, game content 1, broadcast content 2, and art content 3.

[0106] That is, the processor (120) can obtain first score information based on the priority of each first user of each of the multiple content types, without considering the priority of each first user of each of the multiple types. In the example of FIG. 4, the average score of the VOD content type is 0.7388, the average score of the game content type is 0.7069, the average score of the music content is 0.7112, the average score of the broadcast content is 0.5322, and the average score of the art content is 0.4111. However, the processor (120) may not provide recommended content in the order of VOD content, music content, game content, broadcast content, and art content, but may provide recommended content in the order of VOD content 1, game content 2, music content 1, VOD content 3, VOD content 2, game content 1, broadcast content 2, and art content 3.

[0107] According to the embodiments described above with reference to FIG. 4, the electronic device (100) can provide recommended content by mixing various types of content by recommending content to the user by considering the priority between different types of content, without considering the priority of multiple types.

[0108] FIG. 5 is a diagram illustrating a method for providing recommended content based on the access history of other users with similar probability information according to one or more embodiments.

[0109] Although embodiments for providing recommended content suitable for one user (e.g., the first user) have been described above, the electronic device (100) can provide recommended content for the first user by considering probability information regarding the first user and other users.

[0110] Probability information including probability values ​​for multiple attributes and probability values ​​for multiple detailed attributes can be acquired / updated / stored for each user. As shown in FIG. 5, the first probability information for the first user (identical to that shown in FIG. 3) may be different from the probability information for the second user.

[0111] The processor (120) can identify second probability information among probability information for each of a plurality of users, wherein the similarity with the first probability information is greater than or equal to a threshold value, and can identify a second user among the plurality of users that corresponds to the second probability information.

[0112] For example, the processor (120) can obtain a vector [0.8, 0.1, 0.6, 0.8, 0.5, 0.8, 0.1, 0.2] representing probability values ​​by attribute in probability information for the first user, and can obtain a vector [0.2, 0.7, 0.8, 0.3, 0.5, 0.8, 0.1, 0.6] representing probability values ​​by attribute in probability information for the second user. Then, the processor (120) can calculate the similarity between the two obtained vectors. Various techniques such as cosine similarity, Euclidean distance, and Jaccard similarity may be used to calculate the similarity between the two vectors. When the similarity between the two vectors is calculated, the processor (120) can identify whether the probability information for the second user is similar to the probability information for the first user based on whether the calculated similarity is greater than or equal to a threshold value.

[0113] As another example, the processor (120) may use vectors representing probability values ​​for each detailed attribute, as well as vectors representing probability values ​​for each detailed attribute, in calculating similarity. In particular, the highest probability value among the probability values ​​for each detailed attribute may be used to obtain the vector that is the subject of similarity calculation.

[0114] If the probability information for the second user is similar to the probability information for the first user, the processor (120) can identify the probability information for the second user as second probability information and can identify the second user as a user with a tendency similar to the first user.

[0115] When a second user is identified, the processor (120) may provide at least one recommended content to the first user based on third data corresponding to (or representing) the second user's access history for a plurality of contents. In particular, based on the third data, the processor (120) may provide at least one recommended content of a type that is not included in the first user's access history among a plurality of types.

[0116] Although an embodiment related to comparing probability information between a first user and another user has been described above, a second user may be identified by comparing score information between a first user and another user, and the third data of the second user may be used for content recommendation for the first user.

[0117] According to the embodiments described above with reference to FIG. 5, the electronic device (100) can provide recommended content to the first user by utilizing the access history of a second user who has a similar disposition or interest to the first user. Accordingly, the electronic device (100) can provide recommended content even of a type of content that the first user has not accessed in the past by utilizing the access history of the second user, and thus the diversity of recommended content can be significantly improved.

[0118] FIG. 6 is a drawing for illustrating a user interface according to one or more embodiments.

[0119] As described above, in one or more embodiments, the processor (120) may provide recommended content sequentially according to the priority of score information or control the method of displaying recommended content.

[0120] In one or more embodiments, the processor (120) may display the first content and the second content together within the user interface as recommended content, and may adjust the position of the first object corresponding to the first content to be above or to the left of the second object corresponding to the second content. In this case, the processor (120) may adjust the size of the first object corresponding to the first content to be larger than the second object corresponding to the second content.

[0121] For example, if the score for VOD content A (610) is 0.87, the score for music content B (620) is 0.57, and the score for broadcast content C (630) is 0.55, the processor (120) can display VOD content A (610), music content B (620), and broadcast content C (630) together in the user interface as recommended content.

[0122] In the above example, the processor (120) can display an object corresponding to VOD content A (610) at the top of the area where recommended content is displayed in the user interface, as shown in FIG. 6, and can display an object corresponding to music content B (620) and an object corresponding to broadcast content C (630) below VOD content A (610).

[0123] Additionally, the processor (120) may display the size of the object corresponding to VOD content A (610) within the user interface as larger than the object corresponding to music content B (620) and the object corresponding to broadcast content C (630), as illustrated in FIG. 6. The processor (120) may also adjust the size of the object corresponding to the recommended content to be proportional to the size of the score. In one or more embodiments, the user interface illustrated in FIG. 6 may be updated in real time. For example, the user interface may be displayed to provide recommended content. Subsequently, while the user interface is displayed, the recommended content may be updated in real time based on updates to the access history of one or more other users.

[0124] The processor (120) can adjust not only the position and size of the object corresponding to the content, but also the color, graphic effects, etc. of the object corresponding to the content.

[0125] Meanwhile, as shown at the bottom of FIG. 6, the processor (120) may display a message such as "Are you satisfied with the recommended content?" in a part area (640) of the user interface. Then, when information regarding user feedback selecting "satisfied" or "dissatisfied" is received, the processor (120) may train a neural network model (1010) based on the information regarding user feedback. Even if user feedback is not received directly, the processor (120) may estimate user satisfaction based on whether the user accesses the recommended content and train a neural network model (1010) based on the estimated user satisfaction.

[0126] According to the embodiment described above with reference to FIG. 6, the electronic device (100) can provide recommended content to a user and, together with it, provide information on the priority of each recommended content. Accordingly, the electronic device (100) can induce user access and feedback regarding the recommended content, and can further improve the effectiveness of providing recommended content by retraining the neural network model (1010).

[0127] FIG. 7 is a block diagram showing in detail the configuration of an electronic device (100) according to one or more embodiments.

[0128] As illustrated in FIG. 7, the electronic device (100) may further include a communication unit (130), an input unit (140), an output unit (150), and an interface unit (160), in addition to the memory (110) and the processor (120). However, the configurations illustrated in FIG. 1 and FIG. 7 are merely exemplary, and it is understood that in carrying out the present disclosure, new configurations may be added or some configurations may be omitted in addition to the configurations illustrated in FIG. 1 and FIG. 7.

[0129] The communication unit (130) includes a circuit and can perform communication with an external device. Specifically, the processor (120) can receive various data or information from an external device connected through the communication unit (130) and can also transmit various data or information to the external device.

[0130] The communication unit (130) may include at least one of a Wi-Fi module, a Bluetooth module, a wireless communication module, an NFC module, and an Ultra-Wide Band (UWB) module. Specifically, the Wi-Fi module and the Bluetooth module can each perform communication using the Wi-Fi method and the Bluetooth method. When using the Wi-Fi module or the Bluetooth module, various connection information such as SSID is first transmitted and received, and then various information is transmitted and received after establishing a communication connection using this information.

[0131] In addition, the wireless communication module can perform communication according to various communication standards such as IEEE, Zigbee, 3G (3rd Generation), 3GPP (3rd Generation Partnership Project), LTE (Long Term Evolution), and 5G (5th Generation). Furthermore, the NFC module can perform communication using the NFC (Near Field Communication) method, which utilizes the 13.56 MHz band among various RF-ID frequency bands such as 135 kHz, 13.56 MHz, 433 MHz, 860~960 MHz, and 2.45 GHz. Additionally, the UWB module can accurately measure the Time of Arrival (ToA), which is the time it takes for a pulse to reach a target, and the Angle of Arrival (AoA), which is the angle of arrival of the pulse at the transmitting device, through communication between UWB antennas. Accordingly, precise distance and location recognition within an error range of tens of centimeters indoors is possible.

[0132] In one or more embodiments, when information regarding recommended content is obtained, the processor (120) may control the communication unit (130) to transmit information regarding at least one recommended content to an external device, such as a user terminal. Accordingly, information regarding at least one recommended content may be provided to the user through a display, speaker, etc. of the external device. And, when information regarding user feedback is received from the user terminal through the communication unit (130), the processor (120) may train a neural network model (1010) based on the information regarding user feedback.

[0133] Additionally, the processor (120) can obtain user data, second data, data regarding the neural network model (1010), probability information, score information, information regarding recommended content, etc. by receiving them from an external device through the communication unit (130).

[0134] The input unit (140) includes a circuit, and the processor (120) can receive user commands to control the operation of the electronic device (100) through the input unit (140). Specifically, the input unit (140) may be composed of components such as a microphone, a camera, and a remote control signal receiver. Additionally, the input unit (140) may be implemented as a touch screen included in a display. In particular, the microphone can receive a voice signal and convert the received voice signal into an electrical signal.

[0135] In one or more embodiments, the processor (120) can receive user input requesting recommended content, user input requesting a change to recommended content, user feedback on recommended content, etc. through the input unit (140).

[0136] If the input unit (140) includes a microphone, the processor (120) can receive a voice signal corresponding to user input through the microphone, and can obtain user input corresponding to the voice signal using a voice recognition model and a natural language understanding model.

[0137] The microphone may be included in the input section (140) of the electronic device (100), as well as in an external device. When the microphone is included in an external device, the microphone included in the external device may receive an analog voice signal, convert the received analog voice signal into a digital voice signal, and transmit the converted voice signal to the electronic device (100). Additionally, the electronic device (100) may receive a voice signal from the external device through the communication section (130).

[0138] In this case, the electronic device (100) can perform communication with an external device by using at least one of communication modules such as a Wi-Fi module, a Bluetooth module, a wireless communication module, an NFC module, and a UWB (Ultra-Wide Band) module included in the communication unit (130). When performing communication with multiple external devices, different communication modules may be used for each of the multiple external devices, or the same communication module may be used.

[0139] For example, the external device may be a remote control device (i.e., a remote control) for controlling the electronic device (100), or it may be a device capable of installing an application for controlling a smartphone, an artificial intelligence speaker, or other electronic device connected to the electronic device (100). In this case, the external device may transmit control signals and / or voice signals to the electronic device (100) using an application for controlling the electronic device (100). The output unit (150) includes a circuit, and the processor (120) may output various functions that the electronic device (100) can perform through the output unit (150). Also, the output unit (150) may include at least one of a display, a speaker, and an indicator.

[0140] The display can output image data under the control of the processor (120). Specifically, the display can output an image stored in the memory (110) under the control of the processor (120). In particular, the display according to one or more embodiments of the present disclosure may display a user interface stored in the memory (110). The display may be implemented as an LCD (Liquid Crystal Display Panel), OLED (Organic Light Emitting Diodes), etc., and the display may also be implemented as a flexible display, a transparent display, etc. depending on the case. However, the display according to the present disclosure is not limited to a specific type.

[0141] The speaker can output audio data under the control of the processor (120). The indicator can be lit under the control of the processor (120). Specifically, the indicator can be lit in various colors under the control of the processor (120). For example, the indicator can be implemented using LEDs (Light Emitting Diodes), LCDs (Liquid Crystal Display Panels), VFDs (Vacuum Fluorescent Displays), etc., but is not limited thereto.

[0142] In one or more embodiments, when information about recommended content is obtained, the processor (120) can control the output unit (150) to output at least one recommended content.

[0143] In one or more embodiments, the processor (120) may control the display to display a user interface including a plurality of objects corresponding to at least one recommended content based on the first score information. Here, the plurality of objects may be arranged in order according to priority within the user interface.

[0144] If the output unit (150) includes a speaker, the processor (120) can use a speech synthesis model to obtain voice data corresponding to information about recommended content and control the speaker to output the obtained voice data.

[0145] The interface unit (160) can transmit and receive video data and / or audio data in relation to an external device. Specifically, the interface unit (160) may include an input port capable of receiving video data and / or audio data from an external device and an output port capable of transmitting video data and / or audio data to an external device. In particular, if the interface unit (160) is capable of transmitting and receiving both video data and audio data, separate input and output ports capable of transmitting and receiving video data and audio data may be implemented. The interface unit (160) may connect the electronic device and the external device via a wired connection through a cable, but may also connect the electronic device and the external device wirelessly.

[0146] For example, the interface section (160) may include an HDMI (High-Definition Multimedia Interface) module, a USB (Universal Serial Bus) module, etc. The HDMI module is one of the uncompressed digital video / audio interface standards and can provide an interface between an electronic device and an external device that provides content. The USB module can provide a communication system between an electronic device and an external device that provides content using a predefined input / output standard protocol. In addition to the HDMI module and the USB module, the interface section (160) can, of course, be implemented with various modules to provide video / audio data input / output between an electronic device and an external device, such as a DP (Display Port) module, an RGB module, a DVI (Digital Visual Interface) module, and a Thunderbolt module.

[0147] In particular, in one or more embodiments, the processor (120) can control the interface unit (160) to transmit information about the user interface to an external device in order to display a user interface including a plurality of objects corresponding to at least one recommended content on a display of an external device.

[0148] FIG. 8 is a flowchart illustrating a control method of an electronic device (100) according to one or more embodiments.

[0149] Referring to FIG. 8, the electronic device (100) can obtain first data corresponding to the access history of a first user for a plurality of contents classified according to a plurality of types, and second data corresponding to a plurality of attributes for each of the plurality of types (S810).

[0150] The electronic device (100) can obtain the first data and the second data by loading the first data and the second data stored in the memory (110) of the electronic device (100). The electronic device (100) can also obtain the first data and the second data by receiving the first data and the second data from an external device.

[0151] The electronic device (100) can obtain first score information corresponding to the priority of each of the first users of each of the multiple contents based on first probability information corresponding to the influence of each of the multiple attributes on each of the first users obtained by inputting first data and second data into a neural network model (S820).

[0152] Specifically, the electronic device (100) inputs the first data and the second data into a learned neural network model (1010) to obtain first probability information representing the influence of each of the multiple attributes on the first user.

[0153] In one or more embodiments, the electronic device (100) can obtain first probability values ​​by combining a plurality of second probability values ​​for each attribute.

[0154] In one or more embodiments, when a first user accesses a first content among a plurality of contents, the electronic device (100) can obtain first probability information based on increasing the probability value of at least one attribute corresponding to the first content among a plurality of attributes through a neural network model (1010).

[0155] In one or more embodiments, when a first user accesses a first content among a plurality of contents, if the first content is a recommended content provided based on one of a plurality of attributes, the electronic device (100) can obtain first probability information based on increasing the probability value for one attribute through a neural network model (1010).

[0156] The electronic device (100) can obtain first score information indicating the priority for each of the first users of the plurality of contents based on first probability information.

[0157] In one or more embodiments, the electronic device (100) can obtain first score information indicating the priority for each of the first users of the plurality of contents by assigning a higher score to the content containing more of the attribute / detail attribute that the user considers more when selecting the content, based on at least one of the probability values ​​for each of the plurality of attributes and the probability values ​​for each of the plurality of detailed attributes included in the first probability information.

[0158] In one or more embodiments, the electronic device (100) may obtain first score information by using only the probability values ​​for multiple attributes, without using the probability values ​​for multiple detailed attributes included in the first probability information. Additionally, the electronic device (100) may obtain first score information by using the probability values ​​for multiple attributes together with the probability value corresponding to the detailed attribute with the highest probability value among the probability values ​​for multiple detailed attributes included in the first probability information.

[0159] The electronic device (100) can provide multiple recommended content for the first user based on the first score information (S830).

[0160] In one or more embodiments, the electronic device (100) can identify (or determine) at least one recommended content for a first user based on first score information and can provide the identified recommended content.

[0161] In one or more embodiments, the electronic device (100) may provide at least one recommended content so that at least one recommended content is displayed in a priority order on a user interface based on first score information.

[0162] The control method of the electronic device (100) according to the above-described embodiment may be implemented as a program and provided to the electronic device (100). In particular, the program including the control method of the electronic device (100) may be stored and provided on a non-transitory computer-readable medium.

[0163] Specifically, in a non-transient computer-readable recording medium comprising a program for executing a control method of an electronic device (100), the control method of the electronic device (100) may include the steps of: acquiring first data representing the access history of a first user for a plurality of contents classified according to a plurality of types, and second data representing a plurality of attributes for each of the plurality of types; inputting the first data and the second data into a learned neural network model to acquire first probability information representing the influence of each of the plurality of attributes on the first user; acquiring first score information representing the priority of each of the plurality of contents on the first user based on the first probability information; and providing at least one recommended content for the first user based on the first score information.

[0164] Although a method for controlling an electronic device (100) and a computer-readable recording medium including a program for executing the method for controlling the electronic device (100) have been briefly described above, this is merely to avoid redundant descriptions, and it is obvious that various embodiments of the electronic device (100) can also be applied to a method for controlling the electronic device (100) and a computer-readable recording medium including a program for executing the method for controlling the electronic device (100).

[0165] The artificial intelligence-related function according to the present disclosure is operated through the processor (120) and memory (110) of the electronic device (100).

[0166] The processor (120) may be composed of one or more processors (120). In this case, the one or more processors (120) may include at least one of a CPU (Central Processing Unit), a GPU (Graphic Processing Unit), and an NPU (Neural Processing Unit), but are not limited to the examples of the processor (120) described above.

[0167] The CPU is a general-purpose processor (120) capable of performing not only general operations but also artificial intelligence operations, and can efficiently execute complex programs through a multi-layer cache structure. The CPU is advantageous for a serial processing method that enables organic linkage between previous and next calculation results through sequential calculations. The general-purpose processor (120) is not limited to the examples described above, except for cases where it is specified as the CPU described above.

[0168] A GPU is a processor (120) for large-scale computations, such as floating-point operations used in graphics processing, and can perform large-scale computations in parallel by integrating a large number of cores. In particular, a GPU may be advantageous for parallel processing methods such as convolution operations compared to a CPU. Additionally, a GPU can be used as a co-processor (120) to complement the functions of a CPU. The processor (120) for large-scale computation is not limited to the examples described above, except for cases where it is specified as the aforementioned GPU.

[0169] The NPU is a processor (120) specialized for artificial intelligence computation using an artificial neural network, and each layer constituting the artificial neural network can be implemented in hardware (e.g., silicon). At this time, since the NPU is designed to be specialized according to the specifications required by the company, it has a lower degree of freedom compared to a CPU or GPU, but it can efficiently process the artificial intelligence computation required by the company. Meanwhile, as a processor (120) specialized for artificial intelligence computation, the NPU can be implemented in various forms such as a TPU (Tensor Processing Unit), an IPU (Intelligence Processing Unit), a VPU (Vision Processing Unit), etc. The artificial intelligence processor (120) is not limited to the examples described above, except for cases specified as the aforementioned NPU.

[0170] Additionally, one or more processors (120) may be implemented as a System on Chip (SoC). In this case, the SoC may further include, in addition to one or more processors (120), a memory (110) and a network interface such as a bus for data communication between the processor (120) and the memory (110).

[0171] When a plurality of processors (120) are included in a System on Chip (SoC) included in an electronic device (100), the electronic device (100) can perform operations related to artificial intelligence (e.g., operations related to learning or inference of an artificial intelligence model) by using some of the processors (120) among the plurality of processors (120). For example, the electronic device (100) can perform operations related to artificial intelligence by using at least one of a GPU, NPU, VPU, TPU, or hardware accelerator specialized for artificial intelligence operations such as convolution operations or matrix multiplication operations among the plurality of processors (120). However, this is merely one embodiment, and it is obvious that operations related to artificial intelligence can be processed using a CPU or a general-purpose processor (120).

[0172] Additionally, the electronic device (100) can perform operations related to artificial intelligence functions using multi-cores (e.g., dual cores, quad cores, etc.) included in a single processor (120). In particular, the electronic device (100) can perform artificial intelligence operations such as convolution operations and matrix multiplication operations in parallel using multi-cores included in the processor (120).

[0173] One or more processors (120) control input data to be processed according to predefined operation rules or artificial intelligence models stored in memory (110). The predefined operation rules or artificial intelligence models are characterized by being created through learning.

[0174] Here, being created through learning means that a predefined rule of operation or an artificial intelligence model of desired characteristics is created by applying a learning algorithm to a number of learning data. Such learning may be performed on the device itself where the artificial intelligence according to the present disclosure is executed, or it may be performed through a separate server / system.

[0175] An artificial intelligence model may be composed of multiple neural network layers. At least one layer has at least one weight value and performs the layer's operation through the result of the operation of the previous layer and at least one defined operation. Examples of neural networks include CNN (Convolutional Neural Network), DNN (Deep Neural Network), RNN (Recurrent Neural Network), RBM (Restricted Boltzmann Machine), DBN (Deep Belief Network), BRDNN (Bidirectional Recurrent Deep Neural Network), Deep Q-Networks, and Transformers; however, the neural networks in this disclosure are not limited to the aforementioned examples except where specified.

[0176] A learning algorithm is a method of training a specific target device (e.g., a robot) using a number of learning data to enable the target device to make decisions or predictions on its own. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, and the learning algorithms in this disclosure are not limited to the aforementioned examples except where specified.

[0177] A device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory storage medium' simply means that it is a tangible device and does not contain a signal (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily. For example, a 'non-transitory storage medium' may include a buffer in which data is stored temporarily.

[0178] According to one or more embodiments, the method according to the various embodiments disclosed herein may be provided as included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) through an application store (e.g., Play Store™) or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product (e.g., downloadable app) may be temporarily stored or temporarily created in a device-readable storage medium such as the memory (110) of a manufacturer's server, an application store's server, or a relay server.

[0179] Each component (e.g., module or program) according to the various embodiments of the present disclosure as described above may be composed of a single or multiple entities, and some of the aforementioned sub-components may be omitted, or other sub-components may be further included in the various embodiments. Generally or additionally, some components (e.g., module or program) may be integrated into a single entity to perform the same or similar functions as those performed by each of the respective components prior to integration.

[0180] Operations performed by a module, program, or other component according to various embodiments may be executed sequentially, in parallel, iteratively, or heuristically, or at least some operations may be executed in a different order, omitted, or other operations may be added.

[0181] Meanwhile, the terms “part” or “module” as used in this disclosure include a unit composed of hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit, for example. A “part” or “module” may be a component formed integrally, or a minimum unit or part thereof that performs one or more functions. For example, a module may be composed of an application-specific integrated circuit (ASIC).

[0182] Various embodiments of the present disclosure may be implemented as software comprising instructions stored on a machine-readable storage medium (e.g., a computer). The machine may include an electronic device (e.g., an electronic device (100)) according to the disclosed embodiments, which is a device capable of calling instructions stored from the storage medium and operating according to the called instructions.

[0183] When the above instruction is executed by a processor, the processor may perform the function corresponding to the instruction directly or by using other components under the control of the processor. The instruction may include code generated or executed by a compiler or an interpreter.

[0184] Although embodiments of the present disclosure have been illustrated and described above, the present disclosure is not limited to the specific embodiments described above. It is understood that various modifications can be made by those skilled in the art without departing from the essence of the present disclosure as claimed in the claims, and such modifications should not be understood individually from the technical spirit or perspective of the present disclosure.

Claims

1. In an electronic device, Memory for storing at least one instruction; and At least one processor coupled to the memory so as to be operable; comprising, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, Acquiring first data corresponding to the access history of the first user for multiple contents classified according to multiple types, and Acquiring second data corresponding to multiple attributes for each of the above multiple types, Based on first probability information corresponding to the influence of each of the plurality of attributes on the first user, obtained by inputting the first data and the second data into a neural network model, first score information corresponding to the priority of each of the plurality of contents on the first user is obtained, and An electronic device that provides at least one recommended content for the first user through a display based on the first score information above.

2. In Paragraph 1, An electronic device that, when the above instructions are executed individually or collectively by the at least one processor, enables the electronic device to provide the at least one recommended content in order according to the priority, based on the first score information, so that the at least one recommended content is displayed in a user interface provided within the display.

3. In Paragraph 1, An electronic device that, when the above instructions are executed individually or collectively by the at least one processor, enables the electronic device to acquire the first score information based on the priority of the first user for each of the plurality of contents, without considering the priority of the first user for each of the plurality of types.

4. In Paragraph 1, The first probability information includes first probability values ​​representing the influence on the first user of each of the plurality of attributes and second probability values ​​representing the influence on the first user of each of the plurality of detailed attributes distinguishing each of the plurality of attributes. The above instructions, when executed individually or collectively by the at least one processor, enable the electronic device to acquire the first probability values ​​based on the plurality of second probability values ​​for each attribute.

5. In Paragraph 1, The above neural network model is, An electronic device that acquires first probability information based on increasing the probability value of at least one attribute corresponding to the first content among the plurality of attributes when the first user accesses the first content among the plurality of contents.

6. In Paragraph 5, The above neural network model is, An electronic device that acquires first probability information based on increasing the probability value for at least one attribute corresponding to the first content, wherein the first content is a recommendation content provided based on one of the plurality of attributes.

7. In Paragraph 1, The above neural network model is, An electronic device that obtains the first probability information by assigning weights to the at least one attribute corresponding to the first content based on time information included in the first data and indicating the time of access.

8. In Paragraph 1, When the above instructions are executed individually or collectively by the at least one processor, the electronic device identifies second probability information among probability information for each of the plurality of users, wherein the similarity with the first probability information is greater than or equal to a threshold value, and Identifying a second user corresponding to the second probability information among the plurality of users above, and An electronic device that provides at least one recommended content to the first user based on third data corresponding to the second user's access history to the plurality of contents.

9. In Paragraph 8, The above instructions, when executed individually or collectively by the at least one processor, enable the electronic device to provide, based on the third data, a type of content not included in the access history of the first user among the plurality of types as one of the at least one recommended content.

10. In Paragraph 1, The above electronic device is, In addition to a communication circuit, When the above instructions are executed individually or collectively by the at least one processor, the electronic device controls the communication circuit to transmit information about the at least one recommendation content to the user terminal of the user, and An electronic device that, when information regarding user feedback is received from the user terminal through the communication circuit, trains the neural network model based on the information regarding the user feedback.

11. In Paragraph 1, The above electronic device is, The above display further includes, When the above instructions are executed individually or collectively by the at least one processor, the electronic device controls the display to display a user interface including a plurality of objects corresponding to the at least one recommended content based on the first score information, and The above plurality of objects are arranged in order according to the priority within the user interface.

12. In a method for controlling an electronic device, A step of obtaining first data corresponding to the access history of a first user for multiple contents classified according to multiple types; A step of obtaining second data corresponding to a plurality of attributes for each of the plurality of types above; A step of obtaining first score information corresponding to the priority of each of the plurality of contents for the first user, based on first probability information corresponding to the influence of each of the plurality of attributes on the first user, obtained by inputting the first data and the second data into a neural network model; and A method for controlling an electronic device comprising the step of providing at least one recommended content for the first user through a display based on the first score information.

13. In Paragraph 12, The step of providing the above recommended content is, A method for controlling an electronic device comprising: a step of providing at least one recommended content such that, based on the first score information, the at least one recommended content is displayed in order according to the priority in a user interface provided within the display.

14. In Paragraph 12, The step of obtaining the above-mentioned first score information is, A control method for an electronic device that obtains first score information based on the priority of each of the plurality of contents for each of the first user, without considering the priority of each of the plurality of types for each of the first user.

15. In Paragraph 12, The first probability information includes first probability values ​​representing the influence of each of the plurality of attributes on the first user, and second probability values ​​representing the influence of each of the plurality of detailed attributes distinguishing each of the plurality of attributes on the first user. The step of obtaining the above-mentioned first probability information is, A method for controlling an electronic device comprising the step of obtaining the first probability values ​​based on the second probability values ​​for each of the plurality of attributes.

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