Information processing system, information processing method and program

Abstract

An information processing system, an information processing method, and a program are provided that present a job seeker with subjects of interest to the job seeker based on the job seeker's viewing status of videos related to the job seeker. [Solution] An information processing system in which a management server is communicatively connected to employer terminals and job seeker terminals via a communication network to support employers, and the management server 2 comprises a viewing history memory unit that stores the job seeker's viewing history of videos related to employers that the job seeker has viewed, an interest identification unit that identifies the job seeker's interests based on the viewing history, and a provision unit that provides the interests to employers.

Description

[Technical Field]

[0001] The present invention relates to an information processing system, an information processing method, and a program. [Background technology]

[0002] Video viewing behavior is analyzed to provide personalized videos (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2018-092517 Summary of the Invention [Problem to be solved by the invention]

[0004] In the system described in Patent Document 1, videos are personalized according to the viewing conditions of the viewer, but it is difficult for the user to understand what the viewing conditions were.

[0005] The present invention has been made in view of the above background, and aims to provide a technique that can present the viewing status of a video. [Means for solving the problem]

[0006] The main invention of the present invention for solving the above problem is an information processing system comprising: a viewing history storage unit that stores a viewing history of videos related to employers that a job seeker has viewed; an interest identification unit that identifies an interest of the job seeker based on the viewing history; and a provision unit that provides the interest to the employer.

[0007] Other problems and solutions disclosed in this application will be made clear in the section on preferred embodiments of the invention and the drawings. [Effects of the Invention]

[0008] According to the present invention, it is possible to present the viewing status of a video. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating an example of the overall configuration of an information processing system. [Figure 2] FIG. 2 illustrates an example of a hardware configuration of a management server 2. [Figure 3] FIG. 2 illustrates an example of the software configuration of a management server 2. [Figure 4] 1 is a diagram showing an example of a video playback screen 10 on a job seeker terminal 3. FIG. [Figure 5] FIG. 10 is a diagram illustrating an example in which a video is clicked. [Figure 6] FIG. 10 is a diagram illustrating the operation of the management server 2. DETAILED DESCRIPTION OF THE INVENTION

[0010] <System Overview> An information processing system according to one embodiment of the present invention will be described below. The information processing system of this embodiment is intended to support employers by having job seekers view videos of company information and the like in advance, analyzing the job seekers' video viewing habits, and providing the results to employers so that they can use them as reference for job interviews and the like.

[0011] 1 is a diagram showing an example of the overall configuration of an information processing system. The information processing system of this embodiment is configured to include a management server 2. The management server 2 is communicably connected to a recruiter terminal 1 and a job seeker terminal 3 via a communication network. The communication network is, for example, the Internet, and is constructed using a public telephone line network, a mobile phone line network, a wireless communication path, Ethernet (registered trademark), etc.

[0012] The recruiter terminal 1 and the job seeker terminal 3 are computers operated by the recruiter and the job seeker, respectively. The recruiter terminal 1 and the job seeker terminal 3 may be, for example, a smartphone, a tablet computer, or a personal computer.

[0013] The management server 2 may be a general-purpose computer such as a workstation or a personal computer, or may be logically realized by cloud computing.

[0014] <Administration Server> FIG. 2 is a diagram illustrating an example of the hardware configuration of the management server 2. Note that the illustrated configuration is an example, and other configurations may also be used. The management server 2 includes a CPU 201, a memory 202, a storage device 203, a communication interface 204, an input device 205, and an output device 206. The storage device 203 stores various data and programs, and is, for example, a hard disk drive, a solid state drive, or a flash memory. The communication interface 204 is an interface for connecting to a communication network, and is, for example, an adapter for connecting to Ethernet (registered trademark), a modem for connecting to a public telephone network, a wireless communication device for wireless communication, or a USB (Universal Serial Bus) connector or an RS232C connector for serial communication. The input device 205 is used to input data, and is, for example, a keyboard, a mouse, a touch panel, a button, a microphone, or the like. The output device 206 is used to output data, and is, for example, a display, a printer, a speaker, or the like. Each functional unit of the management server 2 described below is realized by the CPU 201 reading a program stored in the storage device 203 into the memory 202 and executing it, and each storage unit of the management server 2 is realized as part of the storage area provided by the memory 202 and the storage device 203.

[0015] 3 is a diagram illustrating an example of the software configuration of the management server 2. The management server 2 includes a video information storage unit 231, a related information storage unit 232, a viewing history storage unit 233, a video distribution unit 211, an interest target identification unit 212, a provision unit 213, a reaction area acquisition unit 214, a behavior analysis unit 215, and a needs analysis unit 216.

[0016] <Storage section> The video information storage unit 231 stores information about each video (hereinafter referred to as video information). The video information can include video data for playing the video. The video information can include a video ID, which is identification information for the video. The video information can include content data indicating the content of the video. The content data can be, for example, text data explaining the content of the video. When video data is stored as a file, the path to the file and the file name can be used as the video ID, and the file name can include text (content data) indicating the content of the video.

[0017] The related information storage unit 232 stores information related to videos (hereinafter referred to as related information). The related information can include data (related data) for displaying information related to videos. The related data can be, for example, text data, image data, videos, or screen data such as web pages. The related data can also be a URL where a web page, video, or the like is stored. The related data can also be information for displaying a form for data input on a video (this can be screen data written in HTML or the like, or instructions for displaying a form in a program previously transmitted to the job seeker terminal 1). The related information can also include information for displaying a tag at a specific position during a specific period of time while the video is being played. For example, the related information can include information identifying the related data (e.g., a related data ID), information identifying the video (e.g., a video ID), a period for displaying the tag, information identifying an area on the video display screen corresponding to the tag (this can be specified, for example, by the vertices or lengths of a rectangle, circle, polygon, or the like; the unit can be, for example, pixels), the related data, and content data. The period can be specified, for example, by the start time and end time (both of which may be relative times from the start time of playback) during which the tag is displayed within the time from the start to the end of playback of the video. The content data is information that explains the content of the related data, and can be, for example, text data that explains the content of the related data. Note that the tag may be transparent (invisible).

[0018] The viewing history storage unit 233 stores information regarding the viewing history of videos related to employers that a job seeker has viewed (hereinafter, viewing history information). The viewing history information is created regarding the job seeker's viewing of videos, the actions the job seeker has taken while viewing the videos, and the job seeker's viewing of related information. The viewing history information includes information that identifies the job seeker (e.g., a user ID), date and time, information that identifies the video (e.g., a video ID), and action data. The action data included in the viewing history information regarding the job seeker's viewing of videos includes information indicating that the video has been viewed. The action data included in the viewing history information regarding the job seeker's taking an action while viewing a video may include, for example, information indicating that the job seeker has clicked (or tapped) on the video and information indicating the clicked area. The action data included in the viewing history information regarding the job seeker's viewing of related information may include information indicating that the job seeker has viewed related data and information that identifies the related data (e.g., a related data ID). In addition, if a job seeker fills out a form displayed on the video, the action data can include information indicating that the job seeker has filled out the form and the data that was entered (form data).

[0019] <Functional section> The video distribution unit 211 distributes videos to job seekers. The video to be distributed is assumed to be specified in advance by the employer. Alternatively, for example, the employer may provide the job seeker with a URL including information identifying the video (e.g., a video ID) using a QR code (registered trademark) or the like, and the job seeker may access the URL from the job seeker terminal 3 to specify the video to be distributed. The video distribution unit 211 creates screen data for playing the video and transmits it to the job seeker terminal 3, and can then transmit the video data in response to a request from the job seeker terminal 3. The screen data can be written in, for example, HTML. The screen data can include, for example, a JavaScript program that runs on the job seeker terminal 3. The video distribution unit 211 can transmit the screen data accompanied by JavaScript code that is programmed to transmit the position on the video where the job seeker clicked to the management server 2 when the job seeker clicks on the video.

[0020] 4 is a diagram showing an example of a video playback screen 10 on the job seeker terminal 3. The playback screen 10 has a video display field 11, and a video 111 distributed by the video distribution unit 211 is played in the display field 11. A tag 133 may be displayed on the video.

[0021] The interest subject identification unit 212 identifies the interest subject of the job seeker based on the viewing history. The interest subject identification unit 212 can identify the interest subject according to the video viewed by the job seeker. The interest subject identification unit 212 can identify the interest subject based on, for example, content data included in content information corresponding to the video viewed by the job seeker (video ID indicated by the viewing history information). The interest subject identification unit 212 can reproduce the interest subject of the job seeker by, for example, reading the viewing history information corresponding to the job seeker from the viewing history storage unit 233 and providing the large-scale language model with a prompt including the read viewing history information and an instruction to explain the interest subject from the viewing history.

[0022] The subject of interest identified by the subject of interest identifying unit 212 can include, for example, the following information: (1) Information about the type of job the job seeker is interested in Job classifications such as sales, technical, and planning positions Specific job duties in the job category Required skills and qualifications Career path prospects (2) Information about the company culture and environment that job seekers are interested in ·Work style (flextime, remote work, etc.) Employee benefits Internal atmosphere and interpersonal relationships Training system and human resource development policy (3) Information about business activities that interest job seekers Specific products and services - Positioning of business partners and the market ·Business prospects and growth strategies ·Technological capabilities and competitive advantages (4) Information about benefits that job seekers are interested in Salary system Evaluation system Promotion and salary increase system Overtime and vacation status (5) Information about the characteristics of companies that job seekers are interested in ·Company size History since the company's founding Management philosophy and corporate vision Social contribution activities and environmental initiatives

[0023] The subject of interest identification unit 212 can identify the subject of interest as described above, for example, in the following manner. (1) Identification based on viewing time -Long viewing time for videos on specific topics Repeatedly watching videos about a particular topic ·Watching videos about a specific topic to the end (2) Interaction-based identification Clicking to see related information on a particular topic · Actively seeks out detailed information on a particular topic Pausing and rewinding videos about specific topics (3) Identification based on viewing order ·Starting viewing from a specific topic -Preferentially watching videos about specific topics · Watching videos on related topics consecutively (4) Identification based on viewing patterns - Focusing on a specific topic at a specific time · Regularly check out videos on specific topics ·Watching multiple related topics across multiple platforms

[0024] The interest target identification unit 212 can identify interests more accurately by combining these identification methods. For example, if a job seeker watches a video about "sales work content" for a long time (viewing time), pauses and rewinds the video at the part about "how to negotiate with customers" (interaction), and then watches a video about "how to evaluate sales performance" (viewing order), it can be identified that the job seeker has a strong interest in practical skills and evaluation criteria for sales jobs.

[0025] The interest subject identification unit 212 can also calculate the degree of certainty of the identified interest subject. The degree of certainty can be calculated taking into consideration the following factors, for example. (1) Number of views and viewing time (2) Number of times related information has been checked (3) Continued interest in similar topics (4) Consistency of viewing behavior (5) Recent viewing trends The certainty level can be expressed as a numerical value from 0 to 100 or as a graded rating such as "high," "medium," or "low." The providing unit 213 can provide the certainty level to the recruiter along with the identified subject of interest.

[0026] An example of a prompt that the subject of interest identifier 212 provides to the large-scale language model is shown below. Analyze job seekers' interests by viewing their viewing history below: -Videos watched: [Video ID], [Title], [Watching time] Clicked related information: [Related information ID], [Content] Viewing patterns: [viewing order], [interactions] format: 1. Main Interests (in order of confidence) 2. Viewing behavior that supports each interest 3. Possible complementary interests

[0027] The subject of interest identification unit 212 may identify a subject of interest from the viewing history using the following method, without using a large-scale language model.

[0028] (1) Scoring-based identification method The interest subject identification unit 212 can assign scores to various actions in the viewing history and identify the interest subject based on the accumulated scores. Scores can be set for the following elements. Completed watching the video: 10 points Video viewing time (per minute): 1 point Related information clicks: 5 points Pause video: 2 points Video rewind: 3 points Fast forwarding videos: -1 point Consecutive viewing of videos in the same category (per video): 3 points The score of each element can be weighted, for example, using the following formula: Overall score = Σ(score of each element × weighting coefficient) The weighting coefficient can be adjusted, for example, by the following factors: -The more recent the action, the higher the weighting factor. The more consistent the behavior within the same category, the higher the weighting coefficient. -Adjust weighting factors according to viewing order

[0029] (2) Clustering-based identification method The interest subject identification unit 212 can generate a feature vector from the viewing history and identify the interest subject by clustering. The interest subject identification unit 212 can generate the feature vector from, for example, the viewing time for each category, the number of clicks for each category, the viewing completion rate for each category, and the time-series viewing pattern. The interest subject identification unit 212 can also identify clusters by a clustering method such as k-means clustering, hierarchical clustering, or density-based clustering, and extract candidate interest subjects from the centers of the identified clusters.

[0030] (3) Identification method based on sequence analysis The interest subject identification unit 212 can identify the interest subject by analyzing the order of actions and time series patterns in the viewing history. The interest subject identification unit 212 can identify the interest subject by, for example, analysis using a Markov chain model (calculation of state transition probability, analysis of steady state, identification of absorption state) or time series pattern mining (extraction of frequent patterns, analysis of periodicity, analysis of trends).

[0031] (4) Hybrid system The subject of interest identification unit 212 can identify subjects of interest with higher accuracy by combining the above-mentioned methods. For example, the following combinations are possible. Combining scoring and clustering -Use the score as an element of the feature vector -Correct the score with the clustering results Combining clustering and sequence analysis -Use time series patterns as features for clustering -Intra-cluster sequence analysis Combining scoring and sequence analysis -Score adjustment based on sequence patterns -Analysis of score fluctuations over time

[0032] The objects of interest identified by these methods can be structured in the following form: (1) Hierarchical structure Major categories: job type, corporate culture, benefits, etc. · Middle classification: specific job content, working style, salary system, etc. Subcategory: detailed business content, specific systems, specific conditions, etc. (2) Relevance Main Interests Secondary interests Potential Interests (3) Chronology Early interests · Interests after change · Sustained interest

[0033] The providing unit 213 can provide the identified subject of interest to the recruiter.

[0034] The reaction area acquisition unit 214 acquires the area on the display screen of the video to which the job seeker responded. The reaction area acquisition unit 214 can, for example, receive the position on the video that was clicked from the screen data job seeker terminal 3, search the related information storage unit 232 for related information that corresponds to the distributed video and the area that includes the received position, and if the related information is found, can detect that the job seeker responded to the area specified in the related information.

[0035] When a specific area is clicked, the video distribution unit 211 can distribute related data to the job seeker terminal 3. FIG. 5 is a diagram illustrating an example in which a video is clicked. When a tag 133 is clicked (tapped in the example of FIG. 5) in the video display field 11 on the playback screen 10, the reaction area acquisition unit 214 determines that the area corresponding to the tag 133 has been clicked, and the video distribution unit 211 distributes related data included in the searched related information to the job seeker terminal 3, and can display related information 112 in the display field 11 instead of the video 111 or by superimposing it on the video 111. Note that, in the example of FIG. 5, the related information 112 is a still screen, but it is also possible to play the related information 112 as a video, instead of the video 111 that was originally distributed, or by superimposing it on the video 111.

[0036] The subject of interest identifying unit 212 may identify the subject of interest based on the related data corresponding to the area acquired by the reaction area acquiring unit 214. The subject of interest identifying unit 212 may identify the subject of interest based on the video watched by the job seeker and the related information to which the job seeker responded. The subject of interest identifying unit 212 can identify the subject of interest based on, for example, content data corresponding to the video watched by the job seeker and content data of the related data corresponding to the area to which the job seeker responded. The subject of interest identifying unit 212 can generate the subject of interest based on the content of the video and the related information by providing, for example, a prompt to the large-scale language model, the prompt including the content data corresponding to the video watched by the job seeker, the content data of the related data corresponding to the area to which the job seeker responded, and an instruction to identify the subject of interest of the job seeker based on these content data.

[0037] The behavioral analysis unit 215 can analyze the viewing behavior of the job seeker based on the viewing history. For example, the behavioral analysis unit 215 can provide a prompt including the viewing history and an instruction to explain the viewing behavior of the job seeker based on the viewing history to the large-scale language model to generate an explanation of the viewing behavior.

[0038] An example of a prompt that the behavior analysis unit 215 provides to the large-scale language model is shown below. Get insights into job seekers' viewing behavior for HR professionals using the following viewing histories: Overall viewing trends: [Viewing time distribution] Points of interest: [Click information] · Viewing context: [viewing order] format: 1. Characteristics of viewing behavior 2. Distinctive Interactions 3. Interpreting viewing patterns

[0039] The needs analysis unit 216 can also analyze the job seeker's needs based on the viewing history. For example, the needs analysis unit 216 can generate a need description by providing a prompt including the viewing history and an instruction to explain the job seeker's job-seeking needs based on the viewing history to a large-scale language model.

[0040] An example of a prompt that the needs analysis unit 216 provides to the large-scale language model is shown below. Analyze the needs of job seekers from employers by looking at the following viewing history: · Contents that were mainly viewed: [Content category] Confirmed details: [Related information] · Viewing reaction: [Interaction pattern] format: 1. Primary Needs 2. Specific needs 3. Prioritizing needs

[0041] For example, if a viewer watches a video about a sales job and related data, and then watches related data on that detail, a behavioral analysis could generate an explanation of the job seeker's viewing behavior that is easy for employers to read, such as, "The viewer showed a strong interest in information related to sales jobs, and in particular, clicked the details button to select a story. Furthermore, the use of the pagination function to move forward and backward through the information suggests a desire for deeper information." Furthermore, a needs analysis could generate an explanation of the job seeker's needs regarding recruitment that is easy for employers to read, such as, "The viewer is seeking specific information and details about sales jobs and wants to deepen their understanding of the job. In particular, it is assumed that they require specific data and stories about the job content and career path of sales."

[0042] The providing unit 213 can provide the employer with the analysis results related to the job seeker based on the viewing history. The providing unit 213 can provide the employer with the interests of the job seeker. The providing unit 213 can provide the employer with a description of the viewing behavior and / or a description of the needs. The providing unit 213 can, for example, transmit information identifying the job seeker and the interests, description of the viewing behavior, and / or description of the needs of the job seeker to the employer terminal 1.

[0043] <Operation> 6 is a diagram illustrating the operation of the management server 2. The management server 2 distributes videos (S301), registers viewing history information related to the distributed videos (S302), and when the video is clicked (S303: YES), searches for the clicked tag (or its area), distributes corresponding related information (S304), and registers viewing history information related to the related information (S305). The management server 2 creates a description of the job seeker's interests, viewing behavior, and needs based on the viewing history (S306), and can provide these to employers (S307).

[0044] As described above, the information processing system of this embodiment can identify the job seeker's interests based on the job seeker's viewing history of videos that the recruiter has the job seeker view, and provide the recruiter with an explanation of the job seeker's viewing behavior and needs, along with the job seeker's interests. Therefore, the recruiter can use this information to conduct interviews and determine whether to hire the job seeker.

[0045] Although the present embodiment has been described above, the above embodiment is intended to facilitate understanding of the present invention and is not intended to limit the present invention. The present invention may be modified or improved without departing from the spirit thereof, and equivalents thereof are also included in the present invention.

[0046] For example, the processing by each of the functional units of the management server 2 described above may be performed by any of the functional units. Also, a different functional unit that performs part of the processing by each of the functional units described above may be added. Also, the functional units of the management server 2 may be distributed across multiple computers.

[0047] Furthermore, the information stored in each storage unit of the management server 2 may be stored in any of the storage units. That is, the information stored in the above-mentioned multiple storage units may be stored in one storage unit, or part of the information stored in one of the above-mentioned storage units may be stored in another storage unit.

[0048] <Variation 1> In the above-described embodiment, the employer provides an explanation of the job seeker's interests and viewing behavior, an explanation of the job seeker's needs regarding the job search, etc. based on the job seeker's viewing history, but this can also be applied to sales situations.

[0049] For example, a sales representative can have a prospective customer watch a video explaining the company or products in advance, and provide the sales representative with information about the prospect's interests, viewing behavior, and anticipated needs, allowing the sales representative to use this information as a reference for their sales activities.

[0050] The viewing history storage unit 233 in this modification stores the viewing history of sales-related videos viewed by the sales partner. Specifically, it stores the viewing history of product explanation videos, service explanation videos, company explanation videos, etc. It also stores the viewing history of related information such as detailed explanation materials, technical documents, price lists, and case studies related to each video. The viewing history also includes the start time and end time of the video viewing, operation information such as pausing and rewinding during the viewing period, playback speed during viewing, and device information during viewing. Furthermore, it also stores the reaction history to points of interest set in the video, such as product specifications, service features, price, and delivery time.

[0051] The reaction area acquisition unit 214 acquires the area on the video display screen to which the salesperson responded. For example, it can identify the areas to which the salesperson paid particular attention, such as stopping at a section explaining a specific product function, accessing price information multiple times, or checking detailed information on comparisons with competitors. It can also identify which items in related materials such as catalogs and specifications were checked in detail.

[0052] The interest identification unit 212 identifies the interests of the sales partner based on the viewing history. For example, if the sales partner repeatedly watches the portion about the cost reduction effect of the product, it can be determined that the sales partner has a high interest in the return on investment. Also, if the sales partner confirms in detail the explanations about the operation procedures and maintenance services, it can be determined that the sales partner has a high interest in the practical issues that will arise after implementation. Furthermore, if the sales partner has a high interest in the implementation cases of other companies and performance data, it can be determined that the sales partner is focusing on verifying the implementation risks.

[0053] The interest target identification unit 212 can also identify the interest target based on related information corresponding to the acquired area. For example, it can infer the product characteristics and introduction conditions that the sales partner considers important from the detailed items in the specifications that the sales partner has checked or the contents of the technical documents that the sales partner has downloaded. It can also infer budgetary constraints and investment decision criteria by analyzing which items in the price list or estimate have attracted attention.

[0054] In this modification, a viewing detection unit 217 can be newly provided. The viewing detection unit 217 detects that a sales target has viewed a video. The viewing detection unit 217 can send a notification to the sales representative, for example, when playback of the video begins. The viewing detection unit 217 can also send a notification when viewing of the video is completed or when a particularly important part has been viewed. Furthermore, a notification can also be sent when viewing of related materials begins or when important materials are downloaded.

[0055] The providing unit 213 provides the sales representative with information that the viewing detection unit 217 has detected viewing, as well as an explanation of the target of interest, viewing behavior, and needs. This allows the sales representative to make contact by calling the sales destination without missing the opportunity when the sales destination becomes interested in the company's products. It also makes it possible to communicate with the sales destination after preparing effective proposals based on the viewing behavior and needs of the sales destination. Advice to the sales representative at the time of the call may be generated. In this case, the management server 2 may be provided with an advice generating unit that generates advice to the sales representative, and the providing unit 213 can provide the advice generated by the advice generating unit to the sales representative.

[0056] The advice generation unit can generate advice for a sales representative when making a call to the customer being analyzed by providing a prompt to the large-scale language model that includes, for example, at least one of the customer's interests in products, etc. identified by the interest identification unit 212, the description of the customer's video viewing behavior generated by the behavior analysis unit 215, and the description of the customer's needs generated by the needs analysis unit 216, and instructions for generating advice for the sales representative when making a call based on these.

[0057] Furthermore, the providing unit 213 can adjust the content and granularity of the information to be provided depending on the role and authority of the sales target within the organization. For example, it can provide detailed information on technical matters of interest to technical personnel, and information focusing on investment effects and relevance to management issues to management. Also, depending on the stage of progress of the sales negotiation, it can provide information focusing on general interests in the early stages and information focusing on specific issue recognition in the detailed consideration stage.

[0058] Here, the advice generation unit can also generate advice for business negotiations. The advice generation unit can provide the large-scale language model with a prompt including at least one of the customer's interests in products, etc. identified by the interest identification unit 212, the description of the customer's video viewing behavior generated by the behavior analysis unit 215, and the description of the customer's needs generated by the needs analysis unit 216, and an instruction to generate advice for the sales representative when conducting business negotiations based on these, thereby generating advice for the sales representative when conducting business negotiations with the customer that is the subject of analysis.

[0059] According to this modification, sales representatives can conduct sales activities after understanding the interests and needs of their clients in advance, which enables efficient and effective business negotiations. Furthermore, clients can check product and service information at their own pace and contact the sales representative when they are ready, which leads to more productive business negotiations. Furthermore, in organized sales activities, multiple sales representatives can share the interests and needs of clients, which enables consistent proposals and support.

[0060] <Variation 2> In the above-described embodiment, the interest analysis, behavior analysis, and needs analysis are based on the viewing history, but CRM information may be used in addition to the viewing history.

[0061] CRM information may include, for example, transaction status, negotiation history, visit records, proposal details, quotation history, inquiry details, response history, complaint records, email records, telephone records, etc. In this case, the management server 2 is provided with a CRM information storage unit that stores CRM information for each user.

[0062] The interest identification unit 212, the behavior analysis unit 215, and the needs analysis unit 216 can read CRM information corresponding to the customer being analyzed from the CRM information storage unit during analysis and include it in the prompt.

[0063] <Variation 3> In addition to the job seeker's interests to the employer, the description of the job seeker's viewing behavior, and the description of the job seeker's needs in the above-described embodiment, advice for the recruiter may be generated. In this case, the management server 2 is provided with an advice generation unit that generates advice for the recruiter. The advice generation unit can provide the large-scale language model with a prompt that includes at least one of the customer's interests in products, etc. identified by the interest identification unit 212, the description of the customer's video viewing behavior generated by the behavior analysis unit 215, and the description of the customer's needs generated by the needs analysis unit 216, as well as an instruction to generate advice for the recruiter to use when interviewing the job seeker based on these. This allows the large-scale language model to generate advice for the recruiter when interviewing the customer being analyzed.

[0064] <Disclosures> The present disclosure also includes the following configurations. [Item 1] a viewing history storage unit that stores a viewing history of videos related to recruiters that have been viewed by job seekers; an interest identification unit that identifies an interest of the job seeker based on the viewing history; a providing unit that provides the subject of interest to the recruiter; An information processing system comprising: [Item 2] Item 1, an information processing system according to item 1, a related information storage unit that stores related information relating to an area on a display screen of the moving image in association with information that identifies the area; a reaction area acquisition unit that acquires an area on the display screen of the video to which the job seeker responded; Equipped with the interest identification unit identifies the interest based on the related information corresponding to the acquired area; An information processing system characterized by: [Item 3] Item 2. The information processing system according to item 2, a video information storage unit that stores content information indicating the content of each of the videos, the interest identification unit identifies the interest based on the content information corresponding to the video viewed by the job seeker and the related information corresponding to the acquired area; An information processing system characterized by: [Item 4] Item 1, an information processing system according to item 1, a behavior analysis unit that provides the viewing history and a prompt including an instruction to describe the job seeker's viewing behavior based on the viewing history to a large-scale language model to generate a description of the viewing behavior; Equipped with the providing unit providing a description of the viewing behavior; An information processing system characterized by: [Item 5] Item 1, an information processing system according to item 1, a needs analysis unit that provides a large-scale language model with a prompt including an instruction to explain the needs that the job seeker has with the recruiter based on the viewing history and the viewing history, thereby generating an explanation of the needs; Equipped with the providing unit providing a description of the needs; An information processing system characterized by: [Item 6] storing a viewing history of videos about employers viewed by job seekers; identifying interests of the job seeker based on the viewing history; providing said interest to said recruiter; An information processing method characterized by being executed by a computer. [Item 7] storing a viewing history of videos about employers viewed by job seekers; identifying interests of the job seeker based on the viewing history; providing said interest to said recruiter; A program that causes a computer to execute the following.

[0065] <Other disclosures> [Second item 1] a video information storage unit that stores content information indicating the content of the video; a related information storage unit that stores related information relating to an area on a display screen of the moving image in association with information that identifies the area; a viewing history storage unit that stores a viewing history including the content information corresponding to the video viewed by the viewer and the related information corresponding to the area to which the viewer responded while viewing the video; a behavior analysis unit that provides the viewing history and a prompt including an instruction to describe the viewing behavior of the viewer based on the viewing history to a large-scale language model to generate a description of the viewing behavior; An information processing system comprising: [3rd item 1] a video information storage unit that stores content information indicating the content of the video; a related information storage unit that stores related information relating to an area on a display screen of the moving image in association with information that identifies the area; a viewing history storage unit that stores a viewing history including the content information corresponding to the video viewed by the viewer and the related information corresponding to the area to which the viewer responded while viewing the video; a needs analysis unit that provides the viewing history and a prompt including an instruction to describe the viewer's needs based on the viewing history to a large-scale language model to generate a description of the needs; An information processing system comprising: [4th item 1] a viewing history storage unit that stores viewing history of videos related to sales viewed by the sales partner; an interest identification unit that identifies an interest of the sales partner based on the viewing history; a providing unit that provides the subject of interest to a salesperson; An information processing system comprising: [4th item 2] 4. An information processing system according to item 1, a related information storage unit that stores related information relating to an area on a display screen of the moving image in association with information that identifies the area; a reaction area acquisition unit that acquires an area on the display screen of the video to which the sales partner has responded; Equipped with the interest identification unit identifies the interest based on the related information corresponding to the acquired area; An information processing system characterized by: [4th item 3] 4. An information processing system according to item 2, a video information storage unit that stores content information indicating the content of each of the videos, the interest identification unit identifies the interest based on the content information corresponding to the video viewed by the job seeker and the related information corresponding to the acquired area; An information processing system characterized by: [4th item 4] 4. An information processing system according to item 1, a behavior analysis unit that provides a large-scale language model with the viewing history and a prompt including an instruction to explain the viewing behavior of the sales partner based on the viewing history, thereby generating an explanation of the viewing behavior; Equipped with the providing unit providing a description of the viewing behavior; An information processing system characterized by: [4th item 5] 4. An information processing system according to item 1, a needs analysis unit that provides a large-scale language model with a prompt including an instruction to explain the needs that the job seeker has with the recruiter based on the viewing history and the viewing history, thereby generating an explanation of the needs; Equipped with the providing unit providing a description of the needs; An information processing system characterized by: [Explanation of symbols]

[0066] 1. Recruiter terminal 2 Management Server 3 Job Seeker Terminal

Claims

1. a viewing history storage unit that stores a viewing history including information identifying a video viewed by a viewer and information regarding taps or clicks on the video performed by the viewer while viewing the video; an interest identification unit that identifies an interest of the viewer based on the viewing history; An information processing system comprising:

2. 2. The information processing system according to claim 1, The viewing history includes information that identifies the area on the display screen of the video that was tapped or clicked on; An information processing system characterized by:

3. a viewing history storage unit that stores a viewing history including information identifying a video related to a recruiter that a job seeker has viewed and information regarding taps or clicks on the video performed by the job seeker while viewing the video; an interest identification unit that identifies an interest of the job seeker based on the viewing history; An information processing system comprising:

4. 4. The information processing system according to claim 3, The viewing history includes information that identifies the area on the display screen of the video that was tapped or clicked on; An information processing system characterized by:

5. a viewing history storage unit that stores a viewing history including information identifying a sales-related video viewed by a sales partner and information regarding taps or clicks on the video performed by the sales partner while viewing the video; an interest identification unit that identifies an interest of the sales partner based on the viewing history; An information processing system comprising:

6. 6. The information processing system according to claim 5, The viewing history includes information that identifies the area on the display screen of the video that was tapped or clicked on; An information processing system characterized by:

7. a viewing history storage unit that stores a viewing history including information identifying a video viewed by a viewer, information identifying an area on a display screen of the video that was tapped or clicked by the viewer, and related information related to the area that was displayed by the tapping or clicking; an interest identification unit that identifies an interest of the viewer based on the viewing history; An information processing system comprising:

8. a viewing history storage unit that stores a viewing history including information specifying a video related to a recruiter that a job seeker has viewed, information specifying an area on the display screen of the video that has been tapped or clicked by the job seeker, and related information related to the area that has been displayed by the tapping or clicking; an interest identification unit that identifies an interest of the job seeker based on the viewing history; An information processing system characterized by:

9. a viewing history storage unit that stores a viewing history including information identifying a video related to sales that a sales partner has viewed, information identifying an area on the display screen of the video that has been tapped or clicked by the sales partner, and related information related to the area that has been displayed by the tapping or clicking; an interest identification unit that identifies an interest of the sales partner based on the viewing history; An information processing system characterized by:

10. storing a viewing history including information identifying videos viewed by a viewer and information regarding taps or clicks on the videos performed by the viewer while viewing the videos; identifying interests of the viewer based on the viewing history; An information processing method characterized by being executed by a computer.

11. storing a viewing history including information identifying videos related to employers that have been viewed by job seekers and information regarding taps or clicks made by the job seeker on the videos while viewing the videos; identifying interests of the job seeker based on the viewing history; An information processing method characterized by being executed by a computer.

12. Storing a viewing history including information identifying a sales-related video viewed by the sales partner and information regarding taps or clicks on the video performed by the sales partner while viewing the video; Identifying an interest of the sales partner based on the viewing history; An information processing method characterized by being executed by a computer.

13. Storing a viewing history including information identifying a video viewed by a viewer, information identifying an area on a display screen of the video that was tapped or clicked by the viewer, and related information related to the area that was displayed by the tapping or clicking; identifying interests of the viewer based on the viewing history; An information processing method characterized by being executed by a computer.

14. storing a viewing history including information identifying videos related to employers that have been viewed by job seekers, information identifying areas on the display screen of the videos that have been tapped or clicked by the job seekers, and related information related to the areas that have been displayed by the tapping or clicking; identifying interests of the job seeker based on the viewing history; An information processing method characterized by being executed by a computer.

15. Storing a viewing history including information identifying a sales-related video viewed by a sales partner, information identifying an area on a display screen of the video that was tapped or clicked by the sales partner, and related information related to the area that was displayed by the tapping or clicking; Identifying an interest of the sales partner based on the viewing history; An information processing method characterized by being executed by a computer.

16. storing a viewing history including information identifying videos viewed by a viewer and information regarding taps or clicks performed by the viewer while viewing the videos; identifying interests of the viewer based on the viewing history; A program that causes a computer to execute the following.

17. storing a viewing history including information identifying videos related to employers that the job seeker has viewed and information regarding taps or clicks performed by the job seeker while viewing the videos; identifying interests of the job seeker based on the viewing history; A program that causes a computer to execute the following.

18. storing a viewing history including information identifying videos related to sales viewed by the sales partner and information regarding taps or clicks performed by the sales partner while viewing the videos; Identifying an interest of the sales partner based on the viewing history; A program that causes a computer to execute the following.

19. Storing a viewing history including information identifying a video viewed by a viewer, information identifying an area on a display screen of the video that was tapped or clicked by the viewer, and related information related to the area that was displayed by the tapping or clicking; identifying interests of the viewer based on the viewing history; A program that causes a computer to execute the following.

20. storing a viewing history including information identifying videos related to employers that have been viewed by job seekers, information identifying areas on the display screen of the videos that have been tapped or clicked by the job seekers, and related information related to the areas that have been displayed by the tapping or clicking; identifying interests of the job seeker based on the viewing history; A program that causes a computer to execute the following.

21. Storing a viewing history including information identifying a sales-related video viewed by a sales partner, information identifying an area on a display screen of the video that was tapped or clicked by the sales partner, and related information related to the area that was displayed by the tapping or clicking; Identifying an interest of the sales partner based on the viewing history; A program that causes a computer to execute the following.

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