Method, electronic device, program, and storage medium for generating user profile

Abstract

According to the present disclosure, a method may be provided, the method comprising the operations of: obtaining user data on the basis of an interaction between a user and an electronic device; collecting a first plurality of words of interest of the user on the basis of the user data; determining a first plurality of levels of interest of the user for a plurality of subjects on the basis of the first plurality of words of interest; modifying the first plurality of levels of interest using a second plurality of levels of interest of a plurality of users determined for the plurality of subjects on the basis of a second plurality of words of interest of the plurality of users, and thereby obtaining an additional word of at least one subject corresponding to at least one level of interest satisfying a condition among the modified first plurality of levels of interest; and generating a profile of the user on the basis of the first plurality of words of interest and the additional word.

Description

Method, electronic device, program, and storage medium for creating a user profile

[0001] The present disclosure relates to a method, electronic device, program, and storage medium for generating a user profile.

[0002] Electronic devices such as smartphones and tablets can provide customized services to users by utilizing user profiles. For example, a user profile may include information such as the user's age, gender, occupation, current address, birthplace, interests, hobbies, income, goals, marital status, family relationships, preferred content, applications primarily used, and purchase history, and electronic devices can provide personalized services to users by utilizing this various information.

[0003] Information regarding user characteristics may be utilized to build user profiles and provide personalized services. This information can be entered directly by the user. For example, users can directly input their information through the user interface of an electronic device. For instance, information regarding a user's interests or hobbies can be collected through surveys asking about their preferred interests or hobbies. While this method allows for the collection of information about user characteristics in a relatively simple and clear manner, it requires voluntary participation, which can be burdensome for users seeking to protect their privacy and may result in users not responding honestly. Furthermore, this approach may fail to collect sufficient and accurate data from some users, potentially limiting the collection of comprehensive information about users.

[0004] Information regarding user characteristics can be indirectly inferred. For example, information regarding user characteristics can be inferred by analyzing applications installed on an electronic device, web pages visited via the device, keywords searched via the device, content entered into a memo application, and schedules registered in a calendar application. Access to the data analyzed to indirectly infer information regarding user characteristics requires the user's consent, and if the user uses the electronic device infrequently or does not permit data access, the amount of data used for analysis may be limited.

[0005] The information described above may be provided as related art for the purpose of aiding understanding of the present disclosure. No claim or determination is made as to whether any of the foregoing may be applied as prior art in relation to the present disclosure.

[0006] According to one embodiment, a method comprising one or more operations may be provided. The method may include an operation of acquiring user data based on the interaction between a user and an electronic device. The method may include an operation of collecting a first plurality of words of interest of the user based on the user data. The method may include an operation of determining a first plurality of levels of interest of the user for a plurality of subjects based on the first plurality of words of interest. The method may include an operation of acquiring an additional word of at least one subject corresponding to at least one level of interest among the modified first plurality of levels of interest by modifying the first plurality of levels of interest using the second plurality of levels of interest of the multiple users determined for the multiple subjects based on the second plurality of words of interest of the multiple users. The method may include an operation of generating a profile of the user based on the first plurality of words of interest and the additional word.

[0007] According to one embodiment, an electronic device may be provided comprising: a memory for storing instructions; and one or more processors including processing circuitry. When the instructions are executed individually or collectively by the one or more processors, the electronic device may be able to perform one or more operations. The one or more operations may include acquiring user data based on the interaction between a user and the electronic device. The one or more operations may include collecting a first plurality of words of interest of the user based on the user data. The one or more operations may include determining a first plurality of levels of interest of the user regarding a plurality of subjects based on the first plurality of words of interest. The above one or more operations may include obtaining an additional word of at least one topic corresponding to at least one interest satisfying a condition among the modified first multiple interests by modifying the first multiple interests using the second multiple interests of the multiple users determined for the multiple topics based on the second multiple interest words of the multiple users. The above one or more operations may include generating a user profile based on the first multiple interest words and the additional word.

[0008] According to one embodiment, a computer-readable recording medium may be provided that records a program for executing a method comprising one or more operations. The one or more operations may include an operation of acquiring user data based on the interaction between a user and an electronic device; and an operation of collecting a first plurality of words of interest of the user based on the user data. The one or more operations may include an operation of determining a first plurality of levels of interest of the user for a plurality of subjects based on the first plurality of words of interest. The one or more operations may include an operation of acquiring an additional word of at least one subject corresponding to at least one level of interest among the modified first plurality of levels of interest that satisfies a condition by modifying the first plurality of levels of interest using the second plurality of levels of interest of the plurality of users determined for the plurality of subjects based on the second plurality of words of interest of the plurality of users. The one or more operations may include an operation of generating a profile of the user based on the first plurality of words of interest and the additional word.

[0009] A computer-readable non-transitory recording medium according to one embodiment of the present invention may store at least one instruction and / or instruction that causes an electronic device to perform the method or operation of the electronic device described above when executed.

[0010] In relation to the description of the drawings, the same or similar reference numerals may be used for identical or similar components.

[0011] FIG. 1 is a diagram illustrating the process of creating a user profile according to one embodiment.

[0012] FIG. 2 is a diagram illustrating the relationship between a plurality of users and their words of interest according to one embodiment.

[0013] FIG. 3 is a flowchart of a method for creating a user profile according to one embodiment.

[0014] FIG. 4 is a diagram illustrating the process of determining the level of interest regarding a subject according to one embodiment.

[0015] FIG. 5 is a diagram illustrating the process of modifying a user's interest in a subject according to one embodiment.

[0016] FIG. 6 is a diagram illustrating the relationship between interest and additional words according to one embodiment.

[0017] FIG. 7 is a diagram illustrating the process of obtaining additional words according to one embodiment.

[0018] FIG. 8 is a diagram illustrating the process of generating a user profile based on words of interest and additional words according to one embodiment.

[0019] FIG. 9 is a diagram illustrating a process for determining whether to acquire additional words according to one embodiment.

[0020] FIG. 10 is a block diagram of an electronic device according to one embodiment.

[0021] Embodiments of the present disclosure are described below in detail with reference to the attached drawings so that those skilled in the art can easily implement them. However, the present disclosure may be embodied in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly explain the present disclosure in the drawings, parts unrelated to the explanation have been omitted, and similar parts throughout the specification are denoted by similar reference numerals.

[0022] The terms used in this disclosure are described in their current, general form considering the functions mentioned herein; however, they may refer to various other terms depending on the intent of those skilled in the art, case law, or the emergence of new technologies. Accordingly, the terms used in this disclosure should not be interpreted solely by their names, but should be interpreted based on the meaning of the terms and the overall content of this disclosure.

[0023] Additionally, terms such as the first, second, third, ..., Nth may be used to describe various components, but the components should not be limited by these terms. These terms are used for the purpose of distinguishing one component from another.

[0024] Throughout the specification, when a part is described as being "connected" to another part, this includes not only cases where they are "directly connected," but also cases where they are "electrically connected" with other components interposed between them. Furthermore, when a part is described as "including" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0025] Phrases such as "in one embodiment" appearing in various places in this disclosure do not necessarily refer to the same embodiment.

[0026] One embodiment of the present disclosure may be represented by functional block configurations and various processing steps. Some or all of these functional blocks may be implemented by various numbers of hardware and / or software configurations that execute specific functions. For example, the functional blocks of the present disclosure may be implemented by one or more microprocessors or by circuit configurations for a specific function. Additionally, for example, the functional blocks of the present disclosure may be implemented in various programming or scripting languages. The functional blocks may be implemented as algorithms executed on one or more processors. Furthermore, the present disclosure may employ prior art for electronic configuration, signal processing, and / or data processing. Terms such as "mechanism," "element," "means," and "configuration" may be used broadly and are not limited to mechanical and physical configurations.

[0027] Furthermore, the connecting lines or connecting members between the components depicted in the drawings are merely illustrative of functional connections and / or physical or circuit connections. In the actual device, connections between components may be represented by various alternative or added functional connections, physical connections, or circuit connections.

[0028] User data refers to data collected from a user or collected about a user. User data may be generated and recorded in an electronic device based on the interaction between the user and the electronic device. Data expressed in natural language on an electronic device may be used as user data. User data may refer to data for which the user has permitted access and use. According to one embodiment, among user data expressed in natural language collected based on the interaction between the user and the electronic device, the user's words of interest may be collected from user data for which the user has permitted access and use, and a user profile may be created based on the collected words of interest. According to one embodiment, additional words may be acquired based on the collected words of interest, and a user profile may be created based on the collected words of interest and the acquired additional words. According to one embodiment, the level of interest regarding multiple topics may be determined based on the collected words of interest, additional words may be acquired based on the level of interest, and a user profile may be created based on the collected words of interest and the acquired additional words.

[0029] A word of interest refers to a word collected from user data, and a single word of interest may consist of one or more words without a length limit. A word of interest may refer to a word collected from user data at least once or a defined number of times. A user's word of interest may consist of words in the language primarily used by that user. According to one embodiment, the number of times a word of interest has been collected may be recorded, and a weight may be applied to the word of interest as the number of times increases. According to one embodiment, words collected from user data more than a threshold number may be collected as words of interest.

[0030] A user profile may include user characteristics such as age, gender, occupation, current address, place of birth, interests, hobbies, income, goals, marital status, family relationships, preferred content, and preferred application categories, and an electronic device may provide personalized services to the user based on the user profile, that is, the user characteristics included in the user profile. User characteristics may be directly entered into the electronic device by the user or inferred by the electronic device. Parts of the user profile that are not entered by the user may be inferred by the electronic device.

[0031] A topic refers to an individual item dealt with within a specific field. Fields and topics can have a hierarchical relationship, and a single field can encompass multiple topics. For example, a topic in the field of sports can refer to individual sports such as baseball, soccer, or basketball. A topic can encompass its subordinate subtopics, and a topic containing subtopics can be expressed as a field of subtopics. For example, baseball can include subtopics such as baseball leagues, baseball players, baseball rules, player positions, and sports similar to baseball.

[0032] FIG. 1 is a diagram illustrating the process of creating a user profile according to one embodiment.

[0033] Referring to Fig. 1, the user's interest words are collected from the user's user data, and a user profile can be created based on the collected interest words.

[0034] User data (110) refers to data obtained from or about a user. User data (110) may be generated and recorded in an electronic device based on the interaction between the user and the electronic device. Data expressed in natural language in the electronic device may be used as user data (110). User data (110) may refer to data for which the user has permitted access and use.

[0035] According to one embodiment, among user data (110) obtained based on the interaction between a user and an electronic device and expressed in natural language, user interest words (120) are collected from user data (110) for which the user has permitted data access and use, and a user profile (130) can be generated based on the collected interest words (120). If the number of collected interest words (120) is insufficient, the reliability of the user profile (130) generated based on a limited number of interest words (120) may be lower than the reliability of the user profile (130) generated based on a relatively abundant number of interest words (120).

[0036] In order to improve the reliability of the user profile (130), according to one embodiment, additional words may be acquired based on the collected words of interest (120), and the user profile (130) may be generated based on the collected words of interest and the acquired additional words. For example, at least some of the synonyms of the collected words of interest may be acquired as additional words, or additional words may be acquired by inputting a prompt requiring a summary or paraphrase into an LLM (large language model)-based artificial intelligence model along with the collected words of interest.

[0037] The method of acquiring synonyms as additional words or using an LLM-based artificial intelligence model may make it difficult to generate an accurate user profile (130) because only the expression method of the user's words of interest changes, but the content does not change. To compensate for this disadvantage, according to one embodiment, additional words may be acquired based on the words of interest collected from other users as well as the words of interest collected from the user (120), and this will be further explained with reference to FIG. 2.

[0038] The method of obtaining additional words for a specific user based on interest words collected for all users places a heavy burden on the server side, and since the users' interest words may be part of sensitive personal data for the users, the users' privacy may be infringed. In order to reduce the burden on the server side and protect the users' privacy, according to one embodiment, the level of interest in a plurality of topics is determined based on interest words (120) collected for the users, additional words may be obtained based on the level of interest, and a user profile (130) may be created based on the collected interest words and the obtained additional words, which will be further explained with reference to FIGS. 3 to 9.

[0039] According to one embodiment, user data (110) may include behavior data that tracks how the user behaves on a specific platform (application or webpage), such as content input, touch history, function usage history, search history, webpage visit history, activity history, exercise history, and viewing history, or may include data expressed in natural language in the behavior data.

[0040] According to one embodiment, various behavioral data may be obtained by various applications running on an electronic device. For example, the content of a memo entered by a user may be obtained by a memo application. For example, web pages visited by a user and search history may be obtained by a web browsing application. For example, the content of a schedule registered by a user may be obtained by a schedule application. For example, the applications used by a user on the electronic device, the usage time of the applications, and the frequency of use may be recorded by a screen time application. According to one embodiment, the user's activity history or exercise history, such as walking, running, driving, and sports, may be measured or recorded by an exercise application, a map application, a navigation application, or a health application, but is not limited thereto, and may be measured or recorded by various applications.

[0041] According to one embodiment, behavioral data may be archived by an application that acquired the behavioral data, but is not limited thereto, and may be acquired by a manager application that comprehensively stores various types of data. The manager application may run in the background of the user's electronic device.

[0042] According to one embodiment, user data (110) may include log data regarding usage records of the electronic device, such as the time the electronic device is active, the time it is inactive, applications used, application usage time, and application usage frequency, or may include data expressed in natural language in the log data. The log data may include application log data and web log data. The log data may be recorded by the Screen Time application of the electronic device. Application log data may be generated by the Screen Time application or by each respective application. Web log data may be generated by a web browsing application.

[0043] According to one embodiment, user data (110) may include location data such as the user's location, movement path, and places visited, or may include data expressed in natural language in location data. Location data may be measured or recorded by a map application, navigation application, health application, web browsing application, or exercise application of an electronic device.

[0044] According to one embodiment, user data (110) may include personal data for identifying an individual, such as the name, email address, phone number, home address, work address, and user ID of the user or other users. The personal data may be entered directly into the electronic device by the user or may be filled in based on content entered into the electronic device by the user.

[0045] According to one embodiment, user data (110) may be used as user data (110), not only the aforementioned personal data, behavioral data, log data, or location data, but also any data expressed in natural language on an electronic device. For example, tags added by a user or automatically added tags for an image may be used as user data (110).

[0046] Since users often do not allow access to or use of personal data, user interest words are collected based on user data (110) other than personal data, and a user profile (130) can be created based on the collected interest words.

[0047] FIG. 2 is a diagram illustrating the relationship between a plurality of users and their words of interest according to one embodiment.

[0048] FIG. 2 illustrates a table (200) of words of interest collected for each user. Referring to FIG. 1 and FIG. 2, the user of FIG. 1 and the words of interest collected for him / her (120, WORD 1, WORD 2, ..., WORD K) may correspond to the first user (USER 1) of FIG. 2 and the words of interest (WORD 1, WORD 2, ..., WORD K) collected for the first user (USER 1), respectively, and in the table (200) of FIG. 2, shading indicates that the corresponding word has been collected as the word of interest for each user.

[0049] Collaborative filtering technology may be used to obtain additional words for the first user (USER 1) based on words of interest collected for all users (USER 1, USER 2, ..., USER M). For example, other users for whom words of interest similar to the words of interest collected for the first user (USER 1) have been collected may be identified, and words that were collected as words of interest for other users but not for the first user (USER 1) may be obtained as additional words for the first user (USER 1).

[0050] This method places a heavy burden on the server side because it requires collecting and processing the interest words of all users (USER 1, USER 2, ..., USER M) in one place, and user privacy may be compromised as interest words can be part of sensitive personal data. Additionally, since each user's interest words account for an extremely small proportion compared to the total interest words, the sparsity is very high, which may make the application of collaborative filtering technology inefficient.

[0051] In order to reduce the burden on the server side and protect the privacy of the user, according to one embodiment, the level of interest in a plurality of topics is determined based on interest words collected about the user, additional words may be obtained based on the level of interest, and a user profile may be created based on the collected interest words and the additional words obtained, which will be further explained with reference to FIGS. 3 to 9.

[0052] FIG. 3 is a flowchart of a method for creating a user profile according to one embodiment.

[0053] In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0054] According to one embodiment, operations 310, 320, 330, 340, and 350 can be understood as being performed in a processor (1020) of an electronic device (e.g., the electronic device (1001) of FIG. 10).

[0055] According to one embodiment, in operation 310, the electronic device may acquire user data of a user. The user may be referred to as a first user, but is not limited thereto. User data may be acquired from the electronic device based on the interaction between the electronic device and the user. User data may refer to data generated based on the interaction between the electronic device and the user and expressed in natural language. User data may refer to data among the data generated based on the interaction between the electronic device and the user and expressed in natural language, for which the user has permitted access to and use of the data.

[0056] The types and acquisition processes of user data are described in Fig. 1, so a redundant description is omitted.

[0057] According to one embodiment, in operation 320, the electronic device may collect a user's words of interest. The user and the user's words of interest may each be referred to as a first user and a first plurality of words of interest of the first user, but are not limited thereto. Words of interest may be collected based on user data obtained about the user. One or more words of no length limit among user data expressed in natural language may be collected as words of interest. Words of interest may be words that appear once or more than a defined number of times in the user data. According to one embodiment, words collected more frequently than a threshold number in the user data may be collected as words of interest. According to one embodiment, the number of times words of interest have been collected may be recorded, and a weight may be applied to the words of interest as the number of such collections increases.

[0058] According to one embodiment, in operation 330, the electronic device may determine the user's level of interest in a topic. The user, the user's interest words, and the level of interest may each be referred to as a first user, the first user's first plurality of interest words, and the first plurality of interest levels, but are not limited thereto. The user's level of interest in topics may be determined based on the user's interest words, which is explained with further reference to FIG. 4.

[0059] FIG. 4 is a diagram illustrating the process of determining interest in a subject according to one embodiment.

[0060] According to one embodiment, the electronic device can determine the interests of a user (USER 1) regarding a plurality of topics (SUBJECT 1, SUBJECT 2, ..., SUBJECT N). According to one embodiment, the electronic device can receive information regarding a plurality of topics (SUBJECT 1, SUBJECT 2, ..., SUBJECT N) from a server. The information regarding the plurality of topics (SUBJECT 1, SUBJECT 2, ..., SUBJECT N) may include information regarding vectors transformed from the plurality of topics (SUBJECT 1, SUBJECT 2, ..., SUBJECT N).

[0061] Referring to FIG. 4, the user's (USER 1) words of interest (420) can be collected based on the user's (USER 1) user data. The electronic device can convert the user's (USER 1) words of interest (420) into vectors through text embedding. Through text embedding, text can be converted into a single real vector in a finite-dimensional vector space. In the vector space, vectors located close to each other can be treated as having a higher semantic correlation or more similar meaning than vectors located far apart. Vectores converted from texts with low semantic correlation are located far apart in the vector space, and vectors converted from texts with high semantic correlation are located close together in the vector space.

[0062] According to one embodiment, text embeddings may include natural language processing embeddings such as word embeddings, sentence embeddings, or document embeddings. Text embeddings may be performed by a language model, but are not limited thereto.

[0063] According to one embodiment, the words of interest (420) of a user (USER 1) may be converted into vectors through text embedding, and a plurality of topics (SUBJECT 1, SUBJECT 2, ..., SUBJECT N) may be converted into vectors through text embedding, and a similarity between the vectors converted from the words of interest (420) and the vectors converted from the plurality of topics (SUBJECT 1, SUBJECT 2, ..., SUBJECT N) may be calculated. The similarity between two vectors may be measured using the cosine value of the angle between the two vectors, and the cosine value is 1 when the angle is 0 degrees, that is, when the directions of the two vectors are the same, and the cosine value for all other angles is less than 1. The similarity between two vectors may be calculated in various ways other than cosine similarity. The similarity between vectors may be expressed as a value within the range of 0 to 1, but is not limited thereto.

[0064] Referring to FIG. 4, similarities (432) between a vector transformed from the first topic (SUBJECT 1) among a plurality of topics (SUBJECT 1, SUBJECT 2, ..., SUBJECT N) and vectors transformed from the user's (USER 1) words of interest (420) can be calculated. For example, the similarity between the first word of interest (WORD 1) and the first topic (SUBJECT 1) can be calculated as 0, the similarity between the second word of interest (WORD 2) and the first topic (SUBJECT 1) as 0.1, the similarity between the third word of interest (WORD 3) and the first topic (SUBJECT 1) as 0.6, and the similarity between the kth word of interest (WORD K) and the first topic (SUBJECT 1) as 0.3. According to one embodiment, the similarity (434) having the highest value among the calculated similarities (432) may be determined as the level of interest of the user (USER 1) in the first subject (SUBJECT 1). According to one embodiment, the level of interest of the user (USER 1) in the first subject (SUBJECT 1) may be calculated as the average or weighted average of the top L similarities among the calculated similarities (432) (L is a natural number greater than 1).

[0065] The table (430) of FIG. 4 shows the interests (430) of a user (USER 1) in a plurality of subjects (SUBJECT 1, SUBJECT 2, ..., SUBJECT N) determined by an electronic device. Referring to Figure 4, the user's interest in the first topic (SUBJECT 1) is 0.6, the user's interest in the second topic (SUBJECT 2) is 0.1, and the user's interest in the Nth topic (SUBJECT N) is 0.0. This indicates that the user has more interest in the first topic (SUBJECT 1) than in the second topic (SUBJECT 2) or the Nth topic (SUBJECT N), and less interest in the Nth topic (SUBJECT N) than in the first topic (SUBJECT 1) or the second topic (SUBJECT 2).

[0066] According to one embodiment, the electronic device can transmit the interests of a user (USER 1) regarding a plurality of topics (SUBJECT 1, SUBJECT 2, ..., SUBJECT N) to a server. The server can receive the interests of a plurality of users regarding a plurality of topics from a plurality of users. Refer to FIG. 5 for a description of the interests of a plurality of users regarding a plurality of topics.

[0067] FIG. 5 is a diagram illustrating the process of modifying a user's interest in a subject according to one embodiment.

[0068] According to one embodiment, the level of interest of a user (USER 1) in a plurality of topics (SUBJECT 1, SUBJECT 2, ..., SBJECT N) can be modified using a plurality of levels of interest (530) of a plurality of users (USER 1, USER 2, ..., USER M) in a plurality of topics (SUBJECT 1, SUBJECT 2, ..., SBJECT N). The plurality of levels of interest (530) of a plurality of users (USER 1, USER 2, ..., USER M) can be determined based on the interest words of the plurality of users (USER 1, USER 2, ..., USER M). The interest words and levels of interest (530) of the plurality of users (USER 1, USER 2, ..., USER M) may each be referred to as a second plurality of interest words and a second plurality of levels of interest (530), but are not limited thereto.

[0069] The server can obtain the modified interests (534) of user (USER 1) by modifying the interests (532) of user (USER 1) regarding multiple topics (SUBJECT 1, SUBJECT 2, ..., SBJECT N) based on multiple interests (530) of multiple users (USER 1, USER 2, ..., USER M) regarding multiple topics (SUBJECT 1, SUBJECT 2, ..., SBJECT N).

[0070] According to one embodiment, the server can obtain the modified interests (534) of the user (USER 1) by performing collaborative filtering modeling (CF modeling) on ​​the multiple interests (530) of multiple users (USER 1, USER 2, ..., USER M) for multiple topics (SUBJECT 1, SUBJECT 2, ..., SUBJECT N).

[0071] According to one embodiment, the server can modify the interests (530) of multiple users (USER 1, USER 2, ..., USER M) through collaborative filtering modeling.

[0072] For example, through collaborative filtering modeling, the interests (532) of user (USER 1) and the interests of other users (USER 2, ..., USER M) are compared to determine users whose interests overlap with those of user (USER 1), and the topics that the determined users are interested in but user (USER 1) is less interested in are determined, so that user (USER 1)'s interests in the determined topics can be adjusted. Collaborative filtering modeling can be performed in various ways so that user (USER 1)'s interests (532) can be adjusted in various ways.

[0073] According to one embodiment, the electronic device transmits the user's (USER 1) interests (532) regarding a plurality of topics (SUBJECT 1, SUBJECT 2, ..., SUBJECT N), which are determined based on the user's interest words, to the server, so the user's privacy can be protected more than when the interest words themselves are transmitted to the server.

[0074] According to one embodiment, the server may obtain additional words of a topic corresponding to an interest that satisfies a condition among the modified interests (534) of the user (USER 1), which is explained with further reference to FIGS. 6 and FIGS. 7.

[0075] FIG. 6 is a diagram illustrating the relationship between interest and additional words according to one embodiment.

[0076] The table (640) of FIG. 6 represents additional words (640) prepared in advance for multiple topics (SUBJECT 1, SUBJECT 2, ..., SUBJECT N). According to one embodiment, the server may use words randomly extracted from words of interest collected from users who have agreed to provide information as additional words (640).

[0077] According to one embodiment, the server may store the collected additional words (640) by distinguishing them according to their similarity (642) with each of the multiple topics (SUBJECT 1, SUBJECT 2, ..., SUBJECT N). For example, if the similarity (642) has a value in the range of 0 to 1, the additional words (640) may be stored by distinguishing them according to the similarity (642) in units of 0.1, but are not limited thereto.

[0078] Referring to FIG. 6, a plurality of topics (SUBJECT 1, SUBJECT 2, ..., SUBJECT N) may be sports included in the field of sports, for example, basketball, soccer, and baseball. According to one embodiment, the server may store only words with a similarity (642) to the topics of 0.5 or higher as additional words (640).

[0079] For example, as illustrated in FIG. 6, additional words (640) may include "basketball" corresponding to a word with a similarity of 1 to the subject (SUBJECT 1) "basketball", words with a similarity of 0.9 or more and less than 1 to "basketball", words with a similarity of 0.8 or more and less than 0.9 to "basketball", words with a similarity of 0.7 or more and less than 0.8 to "basketball", words with a similarity of 0.6 or more and less than 0.7 to "basketball", and words with a similarity of 0.5 or more and less than 0.6 to "basketball", and the server may distinguish and store these words. FIG. 6 illustrates that words with a similarity to the subject with a first value or more and less than a second value have a similarity of the first value, but is not limited thereto, and the server may distinguish and store additional words (640) according to a similarity (642) of 0.01 units.

[0080] According to one embodiment, the server may store a larger number of additional words (640) as words with a lower similarity (642) to the topic. For example, the number of words with a similarity to "basketball" of 0.9 or more and less than 1 may be greater than the number of words with a similarity of 1. According to one embodiment, the server may store only words with a similarity to the topic of 0.5 or more as additional words (640), and the number of words with a similarity to the topic of 0.5, for example, words with a similarity of 0.5 or more and less than 0.6, may be greater than the number of words with a similarity to the topic of 0.6, for example, words with a similarity of 0.6 or more and less than 0.7. According to one embodiment, the number of additional words with a similarity (642) to the topic of a first value may be implemented to be greater than the number of additional words with a similarity (642) to the topic of a second value that is higher than the first value.

[0081] FIG. 7 is a diagram illustrating the process of obtaining additional words according to one embodiment.

[0082] The additional words (740) stored on the server are described in the additional words (640) of FIG. 6, so redundant descriptions are omitted.

[0083] According to one embodiment, the server can obtain additional words (744) of a topic corresponding to an interest that satisfies a condition among the modified interests (734) of the user (USER 1).

[0084] According to one embodiment, among the modified interests (734) of a user (USER 1), the interest having the highest value and the corresponding topic can be identified as the interest and corresponding topic satisfying the condition. Referring to FIG. 7, among the modified interests (734) of a user (USER 1), the interest having the highest value of 0.55 and the corresponding topic (SUBJECT N), "baseball," can be identified. According to one embodiment, additional words (744) corresponding to the identified interest "0.55" and the identified topic "baseball" can be obtained. The additional words (744) corresponding to the identified interest "0.55" and the identified topic "baseball" can be obtained from among additional words (740) stored in the server, distinguished by similarity (742) and topic. For example, as additional words (744) of the topic with an interest level of 0.55 and corresponding topics, words (744) having the same similarity as the topic or an overlapping range of similarity can be obtained.

[0085] According to one embodiment, among the modified interests (734) of a user (USER 1), an interest and a corresponding topic having a value greater than a threshold value can be identified as an interest and a corresponding topic satisfying the condition.

[0086] According to one embodiment, the top P interests and corresponding topics among the modified interests (734) of a user (USER 1) can be identified as interests and corresponding topics that satisfy the conditions (P is a natural number greater than 1).

[0087] According to one embodiment, the server can transmit the acquired additional words (744) to the electronic device of the user (USER 1).

[0088] Referring again to FIG. 3, in operation 340, the electronic device can obtain additional words about the user. According to one embodiment, the electronic device can receive additional words of a topic corresponding to an interest that satisfies a condition among the user's modified interests from a server. The process of obtaining additional words about the user is described through FIG. 4 to 7, so a redundant description is omitted.

[0089] According to one embodiment, in operation 350, the electronic device can generate a user profile of the user. The electronic device can generate a user profile of the user based on the user's interest words collected in operation 320 and additional words obtained in operation 340, which is explained with further reference to FIG. 8.

[0090] FIG. 8 is a diagram illustrating the process of generating a user profile based on words of interest and additional words according to one embodiment.

[0091] A user profile (850) may include user characteristics such as the user's age, gender, occupation, current address, birthplace, interests, hobbies, income, goals, marital status, family relationships, preferred content, and preferred application categories, and an electronic device may provide personalized services to the user based on the user profile (850), that is, the user characteristics included in the user profile (850). User characteristics may be directly entered into the electronic device by the user or may be estimated by the electronic device. Parts of the user profile (850) that are not entered by the user may be estimated by the electronic device.

[0092] According to one embodiment, a user profile (850) of a user may be generated based on words of interest (820) and additional words (844) collected for the user. The process of obtaining additional words (844) is described in FIGS. 4 through 7, so a redundant description is omitted.

[0093] According to one embodiment, the user profile (850) may include user characteristics determined based on words of interest (820) and additional words (844) collected for the user, which is explained with further reference to FIG. 9.

[0094] FIG. 9 is a diagram illustrating a process for determining whether to acquire additional words according to one embodiment.

[0095] According to one embodiment, operations 922, 924, and 926 may be understood to be performed in a processor (1020) of an electronic device (e.g., the electronic device (1001) of FIG. 10).

[0096] According to one embodiment, an electronic device that collects the user's interest words in operation 320 of FIG. 3 can determine the user's user characteristics in operation 922. The electronic device can determine the user characteristics based on the user's interest words collected in operation 320.

[0097] Since an analysis model that estimates user characteristics by analyzing users' interest words may be just one of various conventional analysis models, a description of the analysis model is omitted.

[0098] According to one embodiment, the user characteristics determined in operation 922 may include the age of the user. To determine the age of the user based on the user's interest words, at least one of various analysis models, for example, an age estimation model, may be utilized.

[0099] The electronic device can estimate the user's age by analyzing the user's words of interest through an age estimation model.

[0100] According to one embodiment, in operation 924, the electronic device may determine whether to acquire additional words. For example, the electronic device may determine whether to acquire additional words in operation 924 based on the reliability of the user characteristic determined in operation 922. For example, if the reliability of the user characteristic determined in operation 922 is lower than a threshold reliability, the electronic device may perform operation 330 of acquiring additional words.

[0101] If the confidence of the user characteristic determined in operation 922, for example, the confidence of the user's age estimated by analyzing the user's data through an age estimation model, is lower than the threshold confidence (e.g., 0.6), operation 330 of obtaining additional words can be performed.

[0102] According to one embodiment, in operation 926, the electronic device may determine a field in which additional words are to be acquired. For example, the electronic device may determine a field covering a topic in which additional words are to be acquired. According to one embodiment, the electronic device may determine a field based on user characteristics or types of user characteristics determined in operation 922. For example, if demographic characteristics such as user age, gender, or race are determined in operation 922, a field suitable for analyzing demographic characteristics, for example, sports, shopping, or technology fields, may be determined in operation 926.

[0103] When a field is determined in operation 926, the electronic device can determine the user's interests regarding multiple topics included in the field determined in operation 926, and since this is substantially the same as operation 330 of FIG. 3, a redundant explanation is omitted.

[0104] According to one embodiment, a method comprising one or more operations may be provided. The method may include an operation (310) of acquiring user data (110) based on the interaction between a user and an electronic device. The method may include an operation (320) of collecting a first plurality of words of interest (120, 420, 720, 820) of the user based on the user data. The method may include an operation (330) of determining a first plurality of levels of interest (430, 532) of the user regarding a plurality of subjects based on the first plurality of words of interest. The above method may include an operation (340) of obtaining an additional word (744, 844) of at least one topic corresponding to at least one interest satisfying a condition among the modified first multiple interests (534, 734) by modifying the first multiple interests (530) of the second multiple interests (200) of the multiple users determined for the multiple topics. The above method may include an operation (350) of generating a user profile (850) based on the first multiple interests and the additional word.

[0105] According to one embodiment, a computer-readable recording medium may be provided that records a program for executing the method.

[0106] According to one embodiment, the operation of determining the first plurality of interests of the user for the plurality of topics may include: the operation of determining the plurality of similarities of the first plurality of interest words of the user for the first topic; and the operation of determining the similarity having the highest value among the plurality of similarities as the first interest for the first topic.

[0107] According to one embodiment, the first topic corresponds to a first vector, the first plurality of interest words each correspond to a plurality of vectors, the plurality of similarities are vector similarities of the plurality of vectors with respect to the first vector, and the first interest for the first topic may correspond to the vector similarity having the highest value among the vector similarities.

[0108] According to one embodiment, the first plurality of interests may be modified based on collaborative filtering modeling performed on the second plurality of interests of the plurality of users regarding the plurality of topics.

[0109] According to one embodiment, the first plurality of interests have values ​​in the range of 0 to 1, and among the modified first plurality of interests, the at least one interest satisfying the condition may have a value in the range of 0.5 to 1.

[0110] According to one embodiment, among the modified first plurality of interests, the at least one interest satisfying the condition may include an interest having a value greater than a threshold value among the modified first plurality of interests.

[0111] According to one embodiment, the at least one interest among the modified first plurality of interests satisfying the condition includes a fixed number of interests, and the fixed number of interests may have a higher value than other interests among the modified first plurality of interests.

[0112] According to one embodiment, the method further includes an operation (922) for determining user characteristics included in the user’s profile based on the user’s first plurality of interest words, and an operation (924, 330) for obtaining additional words based on the reliability of the determined user characteristics being lower than a threshold reliability.

[0113] According to one embodiment, the method further includes an operation (926) for determining a field based on the user characteristics, and the plurality of topics may be included in the field.

[0114] According to one embodiment, the operation of acquiring the additional word may include: the operation of transmitting the user’s first plurality of interests regarding the plurality of topics to a server; and the operation of receiving from the server the additional word of the topic corresponding to at least one interest among the modified first plurality of interests that satisfies the condition by modifying the user’s first plurality of interests by the server.

[0115] According to one embodiment, a computer-readable recording medium may be provided that records a program for executing a method comprising one or more operations. The one or more operations may include an operation (310) of acquiring user data (110) based on interaction between a user and an electronic device; and an operation (320) of collecting a first plurality of words of interest (120, 420, 720, 820) of the user based on the user data. The one or more operations may include an operation (330) of determining a first plurality of levels of interest (430, 532) of the user regarding a plurality of subjects based on the first plurality of words of interest. The above one or more operations may include an operation (340) of obtaining an additional word (744, 844) of at least one topic corresponding to at least one interest satisfying a condition among the modified first multiple interests (534, 734) by modifying the first multiple interests using the second multiple interests (530) of the multiple users determined for the multiple topics based on the second multiple interest words (200) of the multiple users. The above one or more operations may include an operation (350) of generating a user profile (850) based on the first multiple interest words and the additional word.

[0116] FIG. 10 is a block diagram of an electronic device (1001) in a network environment (1000) according to various embodiments. Referring to FIG. 10, in the network environment (1000), the electronic device (1001) may communicate with an electronic device (1002) through a first network (1098) (e.g., a short-range wireless communication network) or with an electronic device (1004) or a server (1008) through a second network (1099) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (1001) may communicate with the electronic device (1004) through a server (1008). According to one embodiment, the electronic device (1001) may include a processor (1020), memory (1030), input module (1050), sound output module (1055), display module (1060), audio module (1070), sensor module (1076), interface (1077), connection terminal (1078), haptic module (1079), camera module (1080), power management module (1088), battery (1089), communication module (1090), subscriber identification module (1096), or antenna module (1097). In some embodiments, at least one of these components (e.g., connection terminal (1078)) may be omitted from the electronic device (1001), or one or more other components may be added. In some embodiments, some of these components (e.g., sensor module (1076), camera module (1080), or antenna module (1097)) may be integrated into a single component (e.g., display module (1060)).

[0117] The processor (1020) can, for example, execute software (e.g., program (1040)) to control at least one other component (e.g., hardware or software component) of the electronic device (1001) connected to the processor (1020) and can perform various data processing or operations. According to one embodiment, as at least part of the data processing or operations, the processor (1020) can store commands or data received from other components (e.g., sensor module (1076) or communication module (1090)) in volatile memory (1032), process the commands or data stored in volatile memory (1032), and store the resulting data in non-volatile memory (1034). According to one embodiment, the processor (1020) may include a main processor (1021) (e.g., a central processing unit or an application processor) or an auxiliary processor (1023) that can operate independently or together with it (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, if the electronic device (1001) includes a main processor (1021) and an auxiliary processor (1023), the auxiliary processor (1023) may be configured to use lower power than the main processor (1021) or to be specialized for a specified function. The auxiliary processor (1023) may be implemented separately from the main processor (1021) or as part thereof.

[0118] The auxiliary processor (1023) may control at least some of the functions or states associated with at least one component of the electronic device (1001) (e.g., display module (1060), sensor module (1076), or communication module (1090)) on behalf of the main processor (1021) while the main processor (1021) is in an inactive (e.g., sleep) state, or together with the main processor (1021) while the main processor (1021) is in an active (e.g., application execution) state. According to one embodiment, the auxiliary processor (1023) (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module (1080) or communication module (1090)). According to one embodiment, the auxiliary processor (1023) (e.g., neural network processing unit) may include a hardware structure specialized for processing an artificial intelligence model. The artificial intelligence model may be generated through machine learning. Such learning may be performed, for example, on the electronic device (1001) itself where the artificial intelligence is performed, or through a separate server (e.g., server (1008)). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model may include a plurality of artificial neural network layers.An artificial neural network may be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to the hardware structure, the artificial intelligence model may include a software structure, either additionally or substantially.

[0119] The number of processors (1020) may be one or more. For example, the processor (1020) may have the structure of a multi-core processor such as a dual core, a quad core, or a hexa core.

[0120] The processor (1020) can control the operations of the electronic device (1001) by executing instructions stored in the memory (1030). For example, the processor (1020) may correspond to a plurality of processors that divide and collectively perform a plurality of operations among the processors.

[0121] The memory (1030) can store various data used by at least one component of the electronic device (1001) (e.g., processor (1020) or sensor module (1076)). The data may include, for example, input data or output data for software (e.g., program (1040)) and related commands. The memory (1030) may include volatile memory (1032) or non-volatile memory (1034).

[0122] The program (1040) may be stored as software in memory (1030) and may include, for example, an operating system (1042), middleware (1044), or an application (1046).

[0123] The input module (1050) can receive commands or data to be used for a component of the electronic device (1001) (e.g., processor (1020)) from outside the electronic device (1001) (e.g., user). The input module (1050) may include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0124] The sound output module (1055) can output a sound signal to the outside of the electronic device (1001). The sound output module (1055) may include, for example, a speaker or a receiver. The speaker may be used for general purposes, such as multimedia playback or recording playback. The receiver may be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part thereof.

[0125] The display module (1060) can visually provide information to an external (e.g., user) of the electronic device (1001). The display module (1060) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling said device. According to one embodiment, the display module (1060) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of the force generated by said touch.

[0126] The audio module (1070) can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module (1070) can acquire sound through the input module (1050) or output sound through the sound output module (1055) or an external electronic device (e.g., electronic device (1002)) (e.g., speaker or headphones) connected directly or wirelessly to the electronic device (1001).

[0127] The sensor module (1076) can detect the operating state of the electronic device (1001) (e.g., power or temperature) or the external environmental state (e.g., user state) and generate an electrical signal or data value corresponding to the detected state. According to one embodiment, the sensor module (1076) may include, for example, a gesture sensor, a gyroscope sensor, a barometric pressure sensor, a magnetic sensor, an accelerometer sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biosensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0128] The interface (1077) may support one or more specified protocols that can be used for the electronic device (1001) to be connected directly or wirelessly to an external electronic device (e.g., electronic device (1002)). According to one embodiment, the interface (1077) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0129] The connection terminal (1078) may include a connector through which the electronic device (1001) can be physically connected to an external electronic device (e.g., electronic device (1002)). According to one embodiment, the connection terminal (1078) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0130] The haptic module (1079) can convert an electrical signal into a mechanical stimulus (e.g., vibration or movement) or an electrical stimulus that the user can perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module (1079) may include, for example, a motor, a piezoelectric element, or an electric stimulation device.

[0131] The camera module (1080) can capture still images and video. According to one embodiment, the camera module (1080) may include one or more lenses, image sensors, image signal processors, or flashes.

[0132] The power management module (1088) can manage power supplied to the electronic device (1001). According to one embodiment, the power management module (1088) can be implemented, for example, as at least part of a power management integrated circuit (PMIC).

[0133] The battery (1089) can supply power to at least one component of the electronic device (1001). According to one embodiment, the battery (1089) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0134] The communication module (1090) can support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between an electronic device (1001) and an external electronic device (e.g., electronic device (1002), electronic device (1004), or server (1008)), and the performance of communication through the established communication channel. The communication module (1090) may include one or more communication processors that operate independently of the processor (1020) (e.g., application processor) and support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (1090) may include a wireless communication module (1092) (e.g., cellular communication module, short-range wireless communication module, or GNSS (global navigation satellite system) communication module) or a wired communication module (1094) (e.g., LAN (local area network) communication module, or power line communication module). The corresponding communication module among these communication modules can communicate with an external electronic device (1004) through a first network (1098) (e.g., a short-range communication network such as Bluetooth, WiFi (wireless fidelity) direct, or IrDA (infrared data association)) or a second network (1099) (e.g., a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules may be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (1092) can identify or authenticate the electronic device (1001) within a communication network such as the first network (1098) or the second network (1099) using subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module (1096).

[0135] The wireless communication module (1092) can support 5G networks and next-generation communication technologies following 4G networks, for example, new radio access technology. NR access technology can support high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and connection of multiple terminals (massive machine type communications (mMTC)), or high reliability and low latency (ultra-reliable and low-latency communications (URLLC)). The wireless communication module (1092) can support a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate, for example. The wireless communication module (1092) can support various technologies for securing performance in the high-frequency band, such as beamforming, massive MIMO (multiple-input and multiple-output), full-dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large-scale antenna. The wireless communication module (1092) can support various requirements specified in the electronic device (1001), external electronic device (e.g., electronic device (1004)), or network system (e.g., second network (1099)). According to one embodiment, the wireless communication module (1092) can support a Peak data rate (e.g., 20 Gbps or more) for realizing eMBB, loss coverage (e.g., 164 dB or less) for realizing mMTC, or U-plane latency (e.g., downlink (DL) and uplink (UL) each 0.5 ms or less, or round trip 1 ms or less) for realizing URLLC.

[0136] An antenna module (1097) can transmit a signal or power to or from an external source (e.g., an external electronic device). According to one embodiment, the antenna module (1097) may include an antenna comprising a radiator made of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). According to one embodiment, the antenna module (1097) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as a first network (1098) or a second network (1099), may be selected from the plurality of antennas, for example, by a communication module (1090). A signal or power may be transmitted or received between the communication module (1090) and an external electronic device through the selected at least one antenna. According to some embodiments, in addition to the radiator, other components (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as part of the antenna module (1097).

[0137] According to various embodiments, the antenna module (1097) may form a mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent to a first surface (e.g., bottom surface) of the printed circuit board and capable of supporting a specified high frequency band (e.g., mmWave band), and a plurality of antennas (e.g., array antennas) disposed on or adjacent to a second surface (e.g., top surface or side surface) of the printed circuit board and capable of transmitting or receiving a signal of the specified high frequency band.

[0138] At least some of the above components can be connected to each other via a communication method between peripheral devices (e.g., bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)) and exchange signals (e.g., commands or data) with each other.

[0139] According to one embodiment, commands or data may be transmitted or received between an electronic device (1001) and an external electronic device (1004) through a server (1008) connected to a second network (1099). Each of the external electronic devices (1002, or 1004) may be the same or a different type of device as the electronic device (1001). According to one embodiment, all or part of the operations performed on the electronic device (1001) may be performed on one or more of the external electronic devices (1002, 1004, or 1008). For example, if the electronic device (1001) needs to perform a function or service automatically or in response to a request from a user or another device, the electronic device (1001) may request one or more external electronic devices to perform at least part of the function or service instead of performing the function or service itself or additionally. One or more external electronic devices that receive the above request may execute at least part of the requested function or service, or additional function or service related to the request, and transmit the result of the execution to the electronic device (1001). The electronic device (1001) may provide the result as is or additionally processed as at least part of the response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device (1001) may provide ultra-low latency services using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device (1004) may include an Internet of Things (IoT) device. The server (1008) may be an intelligent server using machine learning and / or neural networks.According to one embodiment, an external electronic device (1004) or server (1008) may be included within the second network (1099). The electronic device (1001) may be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0140] An electronic device (1001) according to one embodiment may correspond to an electronic device in the description of FIGS. 1 to 9, and the electronic device (1001) may perform the operations of an electronic device in the description of FIGS. 1 to 9.

[0141] The technical problems to be solved in this disclosure are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which this disclosure belongs.

[0142] According to one embodiment, an electronic device (1001) may be provided, comprising: a memory (1030) for storing instructions; and one or more processors (1020) including processing circuitry. When the instructions are executed individually or collectively by the one or more processors, the electronic device may be made to perform one or more operations. The one or more operations may include acquiring user data (110) based on the interaction between the user and the electronic device. The one or more operations may include collecting a first plurality of words of interest (120, 420, 720, 820) of the user based on the user data. The one or more operations may include determining a first plurality of levels of interest (430, 532) of the user regarding a plurality of subjects based on the first plurality of words of interest. The above one or more operations may include obtaining an additional word (744, 844) of at least one topic corresponding to at least one interest satisfying a condition among the modified first multiple interests (534, 734) by modifying the first multiple interests using the second multiple interests (530) of the multiple users determined for the multiple topics based on the second multiple interest words (200) of the multiple users. The above one or more operations may include generating a user profile (850) based on the first multiple interest words and the additional word.

[0143] According to one embodiment, when the instructions are executed individually or collectively by the one or more processors, the electronic device may be configured to: determine a plurality of similarities of the user’s first plurality of interest words for a first topic; and determine a similarity having the highest value among the plurality of similarities as a first interest for the first topic.

[0144] According to one embodiment, the first topic corresponds to a first vector, the first plurality of interest words each correspond to a plurality of vectors, the plurality of similarities are vector similarities of the plurality of vectors with respect to the first vector, and the first interest for the first topic may correspond to the vector similarity having the highest value among the vector similarities.

[0145] According to one embodiment, the first plurality of interests may be modified based on collaborative filtering modeling performed on the second plurality of interests of the plurality of users regarding the plurality of topics.

[0146] According to one embodiment, the first plurality of interests have values ​​in the range of 0 to 1, and among the modified first plurality of interests, the at least one interest satisfying the condition may have a value in the range of 0.5 to 1.

[0147] According to one embodiment, among the modified first plurality of interests, the at least one interest satisfying the condition may include an interest having a value greater than a threshold value among the modified first plurality of interests.

[0148] According to one embodiment, the at least one interest among the modified first plurality of interests satisfying the condition includes a fixed number of interests, and the fixed number of interests may have a higher value than other interests among the modified first plurality of interests.

[0149] According to one embodiment, when the instructions are executed individually or collectively by the one or more processors, the electronic device is configured to: determine user characteristics included in the user’s profile based on the user’s first plurality of interest words; determine a field based on the user characteristics; and, based on the reliability of the determined user characteristics being lower than a threshold reliability, perform the operation of acquiring the additional word, and the plurality of topics may be included in the field.

[0150] According to one embodiment, when the instructions are executed individually or collectively by the one or more processors, the electronic device may be configured to: transmit the user’s first plurality of interests regarding the plurality of topics to a server; and receive from the server the additional word of the topic corresponding to at least one interest among the modified first plurality of interests that satisfies the condition by modifying the user’s first plurality of interests.

[0151] The effects obtainable from the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure belongs.

[0152] The electronic device according to the various embodiments disclosed in this document may be of various forms. The electronic device may include, for example, a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a consumer electronics device. The electronic device according to the embodiments of this document is not limited to the devices described above.

[0153] The various embodiments of this document and the terms used therein are not intended to limit the technical features described in this document to specific embodiments, and should be understood to include various modifications, equivalents, or substitutions of said embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of said items unless the relevant context clearly indicates otherwise. In this document, phrases such as "A or B," "at least one of A and B," "at least one of A or B," "A, B or C," "at least one of A, B and C," and "at least one of A, B, or C" may each include any one of the items listed together in the corresponding phrase, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used simply to distinguish said components from other said components and do not limit said components in any other aspect (e.g., importance or order). Where any (e.g., 1st) component is referred to as “coupled” or “connected” to another (e.g., 2nd) component, with or without the terms “functionally” or “communicationly,” it means that said any component may be connected to said other component directly (e.g., via a wire), wirelessly, or through a third component.

[0154] The term “module” as used in the various embodiments of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit, for example. A module may be a component formed integrally, or a minimum unit of said component or a part thereof that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0155] Various embodiments of the present document may be implemented as software (e.g., program (1040)) comprising one or more instructions stored in a storage medium (e.g., internal memory (1036) or external memory (1038)) readable by a machine (e.g., electronic device (1001)). For example, a processor (e.g., processor (1020)) of the machine (e.g., electronic device (1001)) may call at least one of the one or more instructions stored from the storage medium and execute it. This enables the machine to be operated to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code that can be executed by an interpreter. The storage medium readable by the machine may be provided in the form of a non-transitory storage medium. Here, 'non-temporary' simply means that the storage medium 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.

[0156] According to one embodiment, the method according to the various embodiments disclosed herein may be provided by being 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 an application store (e.g., Play Store). TM It can be distributed online (e.g., downloaded or uploaded) through ) or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0157] According to various embodiments, each component (e.g., module or program) of the components described above may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to various embodiments, one or more of the components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Generally or additionally, multiple components (e.g., module or program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as those performed by the corresponding component among the multiple components prior to integration. According to various embodiments, operations performed by the module, program, or other components may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

Claims

1. In electronic devices: Memory for storing instructions; and It includes one or more processors including processing circuitry, and When the above instructions are executed individually or collectively by the one or more processors, the electronic device: Acquire user data based on the interaction between the user and the electronic device; Based on the above user data, collect the user's first plurality of words of interest; Determining the user's first multiple levels of interest regarding multiple subjects based on the first multiple interest words; By modifying the first multiple interests using the second multiple interests of the multiple users determined for the multiple topics based on the second multiple interest words of the multiple users, an additional word of at least one topic corresponding to at least one interest satisfying a condition among the modified first multiple interests is obtained; An electronic device that generates a user profile based on the first plurality of interest words and the additional words.

2. In Paragraph 1, When the above instructions are executed individually or collectively by the one or more processors, the electronic device: Determining multiple similarities of the user's first multiple words of interest regarding the first topic; An electronic device for determining the highest value of the plurality of similarities as a first degree of interest regarding the first subject.

3. In Paragraph 2, The above first subject corresponds to the first vector, and The above-mentioned first plurality of interest words correspond to each of the plurality of vectors, and The above plurality of similarities are vector similarities of the plurality of vectors with respect to the first vector, and The first degree of interest regarding the first subject corresponds to the vector similarity having the highest value among the vector similarities, an electronic device.

4. In Paragraph 1, An electronic device in which the first plurality of interests are modified based on collaborative filtering modeling performed on the second plurality of interests of the plurality of users regarding the plurality of topics.

5. In Paragraph 1, The above-mentioned first plurality of interests have values ​​in the range of 0 to 1, An electronic device in which at least one of the modified first plurality of interests satisfying the above condition has a value in the range of 0.5 to 1.

6. In Paragraph 1, An electronic device wherein at least one interest among the modified first plurality of interests satisfying the condition includes an interest having a value greater than a threshold value among the modified first plurality of interests.

7. In Paragraph 1, Among the above modified first plurality of interests, the at least one interest satisfying the above condition includes a fixed number of interests, An electronic device in which the above-mentioned fixed number of interests has a higher value than other interests among the above-mentioned modified first plurality of interests.

8. In Paragraph 1, When the above instructions are executed individually or collectively by the one or more processors, the electronic device: Determining user characteristics included in the user’s profile based on the user’s first plurality of interest words; An electronic device that obtains the additional word based on the fact that the reliability of the user characteristic determined above is lower than the threshold reliability.

9. In Paragraph 8, When the above instructions are executed individually or jointly by the one or more processors, the electronic device: Determine the field based on the above user characteristics, and The above plurality of subjects are electronic devices included in the above field.

10. In Paragraph 1, When the above instructions are executed individually or collectively by the one or more processors, the electronic device: Transmitting the user's first plurality of interests regarding the above plurality of topics to the server; An electronic device that receives from the server an additional word of the topic corresponding to at least one interest among the modified first plurality of interests satisfying the condition, by modifying the first plurality of interests of the user by the server.

11. An operation to acquire user data based on the interaction between a user and an electronic device; The operation of collecting a first plurality of words of interest of the user based on the above user data; An operation to determine the user's first multiple levels of interest regarding multiple subjects based on the first multiple interest words; An operation of obtaining an additional word of at least one topic corresponding to at least one interest satisfying a condition among the modified first multiple interests by modifying the first multiple interests using the second multiple interests of the multiple users determined for the multiple topics based on the second multiple interest words of the multiple users; and A method comprising the operation of generating a user profile based on the first plurality of interest words and the additional words.

12. In Paragraph 11, The operation of determining the user's first plurality of interests regarding the above plurality of topics is: An operation to determine multiple similarities of the user's first plurality of interest words for a first topic; and A method comprising determining a similarity having the highest value among the plurality of similarities as a first degree of interest regarding the first subject.

13. In Paragraph 12, The above first subject corresponds to the first vector, and The above-mentioned first plurality of interest words correspond to each of the plurality of vectors, and The above plurality of similarities are vector similarities of the plurality of vectors with respect to the first vector, and The first degree of interest regarding the first subject corresponds to the vector similarity having the highest value among the vector similarities, a method.

14. In Paragraph 11, The above method is: An operation to determine user characteristics included in the user’s profile based on the user’s first plurality of interest words; and It further includes an operation to determine a field based on the above user characteristics, and The action of acquiring the above additional word is: Based on the fact that the reliability of the user characteristic determined above is lower than the threshold reliability, the operation of acquiring the additional word is included, The above multiple topics are methods included in the above field.

15. An operation to acquire user data based on the interaction between a user and an electronic device; The operation of collecting a first plurality of words of interest of the user based on the above user data; An operation to determine the user's first multiple levels of interest regarding multiple subjects based on the first multiple interest words; An operation of obtaining an additional word of at least one topic corresponding to at least one interest satisfying a condition among the modified first multiple interests by modifying the first multiple interests using the second multiple interests of the multiple users determined for the multiple topics based on the second multiple interest words of the multiple users; and A computer-readable recording medium having a program for executing a method including the operation of generating a user profile based on the first plurality of interest words and the additional words.

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