JP7444817B2 - information processing equipment - Google Patents

Description

The present invention relates to a technique for analyzing a user using information regarding the user's present and future.

For example, Patent Document 1 discloses a mechanism that infers the subject's interests from information about the subject (posts, comments, shopping history, etc.) and visualizes the subject's lifestyle in chronological order. In this system, questions about the current situation and the future are asked of the subject, and the questions about the current situation include questions related to the subject's current desires and consumption, and questions about the future are asked. The questions include questions that lead to the target's future desires and consumption. By estimating the target person's lifestyle in this way, highly accurate marketing analysis can be performed, and products and services desired by the target person can be proposed with high accuracy.

JP 2018-113023 Publication

An object of the present invention is to focus on the difference between the future desired by each user and the user's current situation, and to perform an analysis on the user by taking that difference into account.

In order to achieve the above object, the present invention includes a processor, a memory, and a communication device, and the processor controls reading and writing of data in the memory and communication by the communication device . Or current information, which is attribute information that each first user who wishes to change jobs has experienced or has at the present time, and information about the hopes, desires, or ideals of the first user in the future. analysis conditions for acquiring user attribute information including at least future information from a first user terminal used by the first user and performing at least one of user extraction, selection, classification, or evaluation; is acquired from a second user terminal used by a second user who is different from the first user and who is recruiting for employment or job change, and the current information included in the acquired user attribute information generating a profile of each of the first users using at least the difference between the information and the future information, and corresponding to the first user's profile generated using the user attribute information and the user attribute information. extracting, sorting , classifying, or evaluating the first user based on the degree of compatibility between the first user's profile and the analysis condition; When generating the profile, the processor performs at least one analysis and outputs information according to the result of the analysis to the first user terminal and the second user terminal; (1) If the acquired current information and future information are evaluated using a common evaluation standard, the first user calculates the difference in evaluation as the difference between the current information and future information. (2) generate a profile of the current information and the future information; (3) creating a profile of the first user by calculating the difference between the current information and the future information; The processor generates the profile of the first user by inputting the difference obtained in (1) or (2) using the acquired current information and the future information, and the processor calculates the degree of suitability. In this case, a machine that uses a pre-prepared user profile and analysis conditions for at least one of user extraction, selection, classification, or evaluation as explanatory variables and a pre-prepared degree of suitability as an objective variable. An information processing device is provided, characterized in that the generated profile of the first user and the acquired analysis conditions are input to the learning model to obtain the degree of compatibility .

According to the present invention, it is possible to analyze each user by taking into consideration the difference between the future desired by each user and the user's current situation.

1 is a block diagram showing an example of the overall configuration of an information processing system 1 according to a first embodiment of the present invention. FIG. 3 is a block diagram showing an example of the hardware configuration of a server device 30 according to the first embodiment. FIG. 3 is a block diagram showing an example of a functional configuration of a server device 30 according to the first embodiment. It is a flowchart showing an example of the operation of the server device 30 according to the first embodiment. FIG. 3 is a diagram illustrating current information and future information of an applicant user according to the first embodiment. FIG. 3 is a diagram illustrating analysis conditions specified by a recruiting user according to the first embodiment. FIG. 2 is a diagram illustrating a profile generated for an applicant user in the first embodiment. FIG. 2 is a diagram illustrating an example of the results of analysis for application users in the first embodiment. It is a figure which illustrates the present information and future information of the recruited user (2nd user) based on 2nd Embodiment. It is a figure which illustrates the analysis conditions specified by the application user (1st user) based on 2nd Embodiment. It is a block diagram showing an example of the functional composition of server device 30 concerning a modification.

[First embodiment]
FIG. 1 is a diagram showing an example of an information processing system 1 according to a first embodiment of the present invention. The information processing system 1 includes a plurality of user terminals (two user terminals 10 and 20 in FIG. 1), a server device 30 functioning as an information processing device according to the present invention, and a wireless communication network or a wired communication network. and a communication network 2. The user terminals 10 and 20 may be computers that users can carry, such as smartphones, wearable terminals, or tablet terminals, or may be fixed computers such as personal computers. In this embodiment, the user terminal 10 is a personal computer that performs wired communication with the communication network 2, and the user terminal 20 is a tablet that performs wireless communication with the communication network 2, but the present invention is not limited to this example. Do not mean.

In this embodiment, the information processing system 1 mediates between a user who desires a job (referred to as an applying user) and a user who performs recruitment (referred to as a recruiting user). According to the information processing system 1, while it is possible for applying users to find jobs that match various values such as the user's purpose in life, characteristics or special skills, outlook on life, and aspirations for the homeland, it is possible for recruiting users to This provides the advantage of being able to secure the human resources that they truly desire. One of the features of the information processing system 1 is that, when mediating between the applicant user and the recruited user, at least a difference between current information regarding the applicant user's present and future information regarding the applicant user's future is used. In addition, in FIG. 1, it is assumed that the recruiting user uses the user terminal 10 and the applying user uses the user terminal 20.

FIG. 2 is a diagram showing the hardware configuration of the server device 30. The server device 30 is physically configured as a computer device including a processor 3001, a memory 3002, a storage 3003, a communication device 3004, an input device 3005, an output device 3006, a bus connecting these devices, and the like. Each of these devices operates using power supplied from a battery (not shown). In addition, in the following description, the word "apparatus" can be read as a circuit, a device, a unit, etc. The hardware configuration of the server device 30 may be configured to include one or more of each device shown in the figure, or may be configured not to include some of the devices. Further, the server device 30 may be configured by communicatively connecting a plurality of devices each having a different housing.

Each function in the server device 30 is performed by loading predetermined software (programs) onto hardware such as a processor 3001 and a memory 3002, so that the processor 3001 performs calculations, controls communication by a communication device 3004, and controls communications by a communication device 3004. This is realized by controlling at least one of data reading and writing in the storage 3003.

The processor 3001, for example, operates an operating system to control the entire computer. The processor 3001 may be configured by a central processing unit (CPU) including an interface with a peripheral device, a control device, an arithmetic device, a register, and the like. Further, for example, a baseband signal processing unit, a call processing unit, etc. may be realized by the processor 3001.

The processor 3001 reads programs (program codes), software modules, data, and the like from at least one of the storage 3003 and the communication device 3004 to the memory 3002, and executes various processes in accordance with these. As the program, a program that causes a computer to execute at least a part of the operations described below is used. The functional blocks of the server device 30 may be realized by a control program stored in the memory 3002 and operated on the processor 3001. Various types of processing may be executed by one processor 3001, or may be executed by two or more processors 3001 simultaneously or sequentially. Processor 3001 may be implemented by one or more chips. Note that the program may be transmitted to the server device 30 via a telecommunications line.

The memory 3002 is a computer-readable recording medium, and includes at least one of ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), and the like. may be done. Memory 3002 may be called a register, cache, main memory, or the like. The memory 3002 can store executable programs (program codes), software modules, etc. for implementing the method according to the present embodiment.

The storage 3003 is a computer-readable recording medium, such as an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (such as a compact disk, a digital versatile disk, or a Blu-ray disk). (registered trademark disk), smart card, flash memory (eg, card, stick, key drive), floppy disk, magnetic strip, etc. Storage 3003 may be called an auxiliary storage device.

The communication device 3004 is, for example, a network device, a network controller, a network card, a communication module, etc., and performs communication via the communication network 2.

The input device 3005 is an input device (eg, key, microphone, switch, button, etc.) that accepts input from the outside. The output device 3006 is an output device (for example, a display, a speaker, an LED lamp, etc.) that performs output to the outside. Note that the input device 3005 and the output device 3006 may have an integrated configuration (for example, a touch panel).

Each device such as a processor 3001 and a memory 3002 is connected by a bus for communicating information. The bus may be configured using a single bus, or may be configured using different buses for each device.

The server device 30 includes hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), and a field programmable gate array (FPGA). A part or all of each functional block may be realized by the hardware. For example, processor 3001 may be implemented using at least one of these hardwares.

The user terminals 10 and 20 are physically configured as computer devices including a processor, a memory, a storage, a communication device, an input device, an output device, a bus connecting these devices, and the like. Each function in the user terminal 20 is achieved by loading predetermined software (programs) onto hardware such as a processor and memory, so that the processor performs calculations, controls communication by communication devices, and controls data in memory and storage. This is realized by controlling at least one of reading and writing. The processor, memory, storage, communication device, input device, output device, and bus connecting these devices are the processor 3001, memory 3002, storage 3003, communication device 3004, input device 3005, output device 3006, and the bus that connects them. The bus that connects these and the hardware are the same.

Note that when the user terminals 10 and 20 perform wireless communication, the communication devices communicate with the computer via the communication network 2 in accordance with communication standards such as LTE (Long Time Evolution) and 5G (5th generation mobile communication system). This is hardware (transmitting/receiving device) for communication between the two. Specifically, the communication devices of the user terminals 10 and 20 use a high frequency switch to realize at least one of frequency division duplex (FDD) and time division duplex (TDD), for example. , a duplexer, a filter, a frequency synthesizer, and the like. For example, a transmitting/receiving antenna, an amplifier section, a transmitting/receiving section, a transmission line interface, etc. may be realized by a communication device. The transmitting and receiving unit may be physically or logically separated into a transmitting unit and a receiving unit.

FIG. 3 is a diagram showing an example of the functional configuration of the server device 30. Each function realized by the server device 30 is achieved by loading predetermined software (programs) onto hardware such as the processor 3001 and memory 3002, so that the processor 3001 performs calculations, controls the communication device 3004, and controls the memory. This is realized by controlling at least one of reading and writing data in the storage 3002 and the storage 3003. Specifically, in the server device 30, functions of a user attribute information acquisition section 31, a profile generation section 32, an analysis condition acquisition section 33, an analysis section 34, and an output section 35 are realized.

The user attribute information acquisition unit 31 acquires user attribute information including at least current information regarding the present of each applicant user and future information regarding the future of the applicant user from the user terminal 20 etc. used by the applicant user via the communication network 2. do. The current information is the applicant user's current quantitative or qualitative attributes, such as the applicant user's current occupation, the school they attend, the organization they belong to, their current activities or actions, and their current hobbies. , information regarding current address, current family composition, current assets, etc. Note that the above information regarding the applicant user's past may be included in the current information from the viewpoint that it forms the current applicant user. In other words, the applicant user's present does not mean the current moment, but is a concept that has a certain time range from the present moment to the past, and the applicant user's current information is the information that the applicant user is experiencing at the present moment. Or the attribute information it has. Therefore, the current information includes a history of the applicant user's location and behavior (for example, information about areas and stores that the applicant has visited, content viewing history, product or service purchase history, usage history, etc.). ) may also be included. The future information is information about the applicant user's hopes, desires, or ideals for the future, and is, for example, information regarding the type of job or work desired by the applicant user.

These current information and future information may be input into the user terminal 20 by the applicant user and transmitted to the server device 30. In addition, the current information and future information are registered in advance in a user database (not shown) connected to the communication network 2 with the consent of the applicant user, and are transmitted from the user database to the server device 30 via the communication network 2. It may be something like that. Further, the current information and the future information may be created and input by a human operation, or may be generated by a recording device that automatically records the user's actions and the like. Since these current information and future information are privacy-related information, it is desirable that they be provided to the server device 30 after being subjected to processing such as encryption, anonymization, or depersonalization.

The profile generation unit 32 generates a profile for each applicant user using at least the difference between the current information and future information included in the user attribute information acquired by the user attribute information acquisition unit 31. The difference between current information and future information is, for example, the current information that the applying user is currently attending school at ○○ or doing the activities of XX as the manager of ○○, and the information that the applicant will be working at △△ in the future. This is a concept that refers to the amount of divergence or distance between current information and future information, which is the degree of divergence from future information that a person wants to engage with.

Such a difference between the current information and the future information can be specified as a difference between the evaluation results when the current information and the future information are evaluated using a common evaluation standard. For example, if the current information is the score p1 of a certain test and the future information is the score p2 of the test, the profile generation unit 32 uses the difference between them, p2-p1, as the difference information. A profile is generated that shows a performance of p2-p1 with respect to the future target score.

Further, the difference between the current information and the future information is determined when the profile generation unit 32 performs a natural language semantic analysis on the current information and the future information, and further develops the analyzed meaning into a predetermined vector space model. can be specified as the difference between the two vectors. For example, if the difference is smaller than a certain threshold, a profile is generated that says, for example, that the applicant user is actively taking on challenges toward his future hopes, or that the applicant user is a hard worker. If the difference is larger than the threshold, a profile will be generated that indicates, for example, that it is not yet clear whether the applicant is serious about his or her future aspirations.

In addition, the profile generation unit 32 performs so-called supervised machine learning to create a learning model using a large number of pairs of profiles (objective variables) and differences (explanatory variables) of current information and future information prepared in advance as training data. The difference between the current information and future information included in the user attribute information acquired by the user attribute information acquisition unit 31 for a certain applicant user is input as an explanatory variable to this learning model, and a profile corresponding to the target variable is generated. You may also obtain

Furthermore, the profile generation unit 32 may generate the profile of the applicant user using the current information and future information themselves, in addition to the difference between the current information and the future information. Regarding this as well, the profile generation unit 32 uses the user attribute information acquisition unit 31 to acquire a learning model generated by machine learning using a large number of pairs of current information and future information prepared in advance and profiles as teacher data. The profile may be obtained by inputting current information and future information included in the user attribute information obtained.

The analysis condition acquisition unit 33 acquires analysis conditions for analyzing the applicant user from the user terminal 10 used by the recruiting user via the communication network 2. In this embodiment, this analysis condition expresses the attributes of the human resources recruited by the recruitment user. For example, the conditions for this analysis are: people who want to work in ○○, people who are interested in Examples include being good at something, being optimistic, having many friends, etc.

The analysis unit 34 compares the profile generated for each applicant user with the analysis conditions acquired from the recruiting users for that applicant user, and performs analysis on the applicant user. The analysis here includes at least one of user extraction, selection, classification, and evaluation. Specifically, the analysis unit 34 calculates the degree or percentage of compliance with the above-mentioned profile and analysis conditions (hereinafter referred to as a score), and selects a predetermined number of users from a large number of applicant users in descending order of their scores. Applicant users are extracted, applicant users whose ratings are equal to or higher than a threshold are selected, or applicant users whose ratings are similar are classified. As a method for the analysis unit 34 to calculate the scores, the analysis unit 34 performs supervised machine learning using, for example, a large number of pairs of profiles and analysis conditions (explanatory variables) prepared in advance and scores (objective variables) as training data. A conceivable method is to generate a learning model in advance, input the profile of the target application user and the analysis conditions of the target recruitment user into the learning model as explanatory variables, and obtain a score as the objective variable.

The output unit 35 outputs information according to the analysis result by the analysis unit 34 to the user terminals 10 and 20 via the communication network 2. Since the information according to the analysis result by the analysis unit 34 is information related to privacy, it is desirable to output the information after being subjected to processing such as encryption, anonymization, or depersonalization. Note that the output destination of the information according to the analysis result by the analysis unit 34 is not limited to only the user terminals 10 and 20.

Next, the operation of the first embodiment will be explained. In the following description, for example, when the server device 30 is described as the main body of processing, specifically, by loading predetermined software (program) onto hardware such as the processor 3001 and memory 3002, the processor 3001 performs calculations and controls communication by the communication device 3004 and reading and/or writing of data in the memory 3002 and storage 3003 to execute processing. The same applies to the user terminals 10 and 20.

In FIG. 4, first, the user attribute information acquisition unit 31 of the server device 30 acquires user attribute information including at least current information and future information of the applicant user from the user terminal 20 etc. used by the applicant user via the communication network 2. (Step S11). The analysis condition acquisition unit 33 also acquires analysis conditions for analyzing the applicant user from the user terminal 10 used by the recruiting user via the communication network 2 (step S11). Either acquisition of user attribute information or acquisition of analysis conditions may be performed first.

Here, FIG. 5 is a diagram illustrating user attribute information of an applicant user who is a university student who is interested in magazine editing. In the example of FIG. 5, the current information is roughly divided into quantitative information and qualitative information, but the present information is not limited to this, and future information may also be roughly divided into quantitative information and qualitative information, and both can be divided into quantitative information and It does not need to be broadly classified into qualitative information. What kind of user attribute information is input can be arbitrarily determined by the applicant user (in this case, the above-mentioned university student). Further, FIG. 6 is a diagram illustrating analysis conditions assuming a case where a certain company in the publishing industry is recruiting human resources. What kind of analysis conditions are specified can be arbitrarily determined by the recruited user (here, a company in the publishing industry). For example, if the recruited user is a company, the analysis conditions may be determined based on the results of a questionnaire survey of the company's employees. As described above, it is desirable that the user attribute information in particular be provided to the server device 30 after being encrypted, anonymized, or depersonalized.

Next, in FIG. 4, the profile generation unit 32 of the server device 30 uses at least the difference between the current information and the future information included in the user attribute information acquired by the user attribute information acquisition unit 31 to generate information about each applicant user. A profile is generated (step S12). The profile generation method is as described above.

Here, FIG. 7 is a diagram illustrating a profile generated based on the user attribute information illustrated in FIG. 5. This profile includes content generated based on the difference between current information and future information (for example, "strong spirit of challenge for the future" and "hard-working type" in Figure 7), as well as content generated based on the current information and future information itself ( In other words, it may include content generated without using the difference between the two (for example, "I am interested in various things" in FIG. 7).

Next, in FIG. 4, the analysis unit 34 of the server device 30 compares the profile generated for each applicant user with the analysis conditions acquired from the recruiting user for that applicant user, and analyzes the applicant user. (Step S13). As for which recruiting user's analysis conditions are to be applied to which applying user, for example, a method may be considered in which the applying user specifies the recruiting user. For example, in the examples shown in Figures 5 to 7, an applicant user who is a university student who is interested in magazine editing may specify a certain company in the publishing industry as a recruiting user, or an applicant user who is a university student who is interested in magazine editing. A possible method (Method 1) is to designate all companies belonging to the publishing industry as recruited users. Furthermore, a method (method 2) is also conceivable in which the analysis unit 34 determines from the user's profile an analysis condition that is highly compatible with the profile. Furthermore, a method (method 3) of applying the analysis conditions of all recruited users to a certain applicant user may also be considered. In particular, according to methods 2 and 3, it is possible to expect a match with a recruiting user who the applying user did not expect to find employment.

Then, in FIG. 4, the output unit 35 of the server device 30 outputs information according to the analysis result by the analysis unit 34 to the user terminals 10, 20 via the communication network 2 (step S14). Here, FIG. 8 shows an example of this analysis result. Information as illustrated in FIG. 8 is displayed on the user terminal 10 used by the recruited user. In this example, on the display screen of the user terminal 10, when you click on the "Current Information", "Future Information", "Profile", and "Analysis Conditions" columns, detailed information about a certain applicant user A will be displayed. It has become. In this example, "50 points" is calculated as the score for this applicant user A. In other words, the degree of compatibility between the applicant user's profile and the analysis conditions specified by the recruiting user is 50 out of 100 points. The recruiting user can refer to this rating and the like to determine whether or not to hire the applicant user. Information similar to that illustrated in FIG. 8 or with some of it omitted may be displayed on the user terminal 20 used by the recruited user. Applying users can refer to this information to determine the degree of compatibility of their own attributes with the recruiting users.

Note that the display of the analysis results on the user terminals 10 and 20 is not limited to the example shown in FIG. For example, the display format can be arbitrarily determined, such as displaying a plurality of recruiting users or application users in a list format, or displaying them preferentially in descending order of scores.

As explained above, in the first embodiment, the user attribute information acquisition unit 31 acquires user attribute information about the applicant user (referred to as the first user), and the profile generation unit 32 acquires the user attribute information about the applicant user (referred to as the first user). The analysis condition acquisition unit 33 acquires the analysis conditions specified by the recruiting user (referred to as the second user), and the analysis unit 34 generates the profile of the applicant user (referred to as the first user). The application user (first user) is analyzed by comparing the profile and the analysis conditions specified by the recruiting user (second user). According to the first embodiment, it is possible to analyze each user by taking into account the difference between the future desired by each user and the user's current situation. As a result, the applicant user can know the recruiting users who are compatible with his or her own attributes, and can also know the direction of the human resources desired by the recruiting user. Furthermore, it becomes possible for the applying user to know the existence of recruiting users who were not expected to be employed. In other words, it becomes possible for the applicant user to know who will evaluate the latent ability that he/she had but was not aware of.

[Second embodiment]
Next, a second embodiment of the present invention will be described. In the first embodiment described above, a profile is generated based on the user attribute information of an applicant user who desires employment, and the applicant user is analyzed by comparing the profile with the analysis conditions of the recruiting user. On the other hand, in the second embodiment, for example, a profile is generated based on the user attribute information of an applicant user who wishes to change jobs, and the applicant At the same time, a profile is generated based on the user attribute information of the recruited user, and the recruited user is analyzed by comparing the profile with the analysis conditions of the applicant user. In other words, while the first embodiment is a one-way process between an applicant user and a recruiting user, the second embodiment is a two-way process between an applicant user and a recruiting user.

The system configuration and device configuration according to the second embodiment are the same as the system configuration (FIG. 1) and device configuration (FIG. 2) of the first embodiment, so a description thereof will be omitted. Further, the functional configuration according to the second embodiment is the same as the functional configuration of the first embodiment (FIG. 3) except that each part performs the same function for both recruiting users and applying users.

In this second embodiment, the applying user corresponds to the "first user" in the present invention, and the recruiting user corresponds to the "second user" in the present invention.

The operation of the second embodiment is the same as the operation of the first embodiment (FIG. 4) except that similar processing is performed for both recruiting users and applying users. That is, in the second embodiment, the user attribute information acquisition unit 31 of the server device 30 acquires user attribute information about the recruiting user (second user) and the applying user (first user) in step S11 of FIG. do. Further, the analysis condition acquisition unit 33 acquires the analysis conditions specified by the recruiting user (second user) and the analysis conditions specified by the application user (first user) in step S11 of FIG. 4.

Here, FIG. 9 is a diagram illustrating current information and future information of the recruited user (second user) according to the second embodiment. What kind of current information and future information is specified can be arbitrarily determined by the recruiting user (second user). For example, if the recruited user (second user) is a company, the current information and future information may be determined based on the results of a questionnaire survey of employees of the company.

Further, FIG. 10 is a diagram illustrating analysis conditions specified by the application user (first user) according to the second embodiment. What kind of analysis conditions are specified can be arbitrarily determined by the applicant user (first user).

Next, the profile generating unit 32 of the server device 30 generates a profile of the recruiting user (second user) and a profile of the applying user (first user) in step S12 of FIG. As a method for generating this profile, the method used in the first embodiment can be used.

Next, in step S13 of FIG. 4, the analysis unit 34 of the server device 30 compares the profile generated for the recruiting user (second user) with the analysis conditions specified by the applying user (first user). Then, analysis is performed on the recruited user (second user). The analysis unit 34 also compares the profile generated for the applicant user (first user) with the analysis conditions specified by the recruiting user (second user), and Perform analysis. As a result, analysis results for both the recruiting user (second user) and the applying user (first user) are obtained. In other words, a score is calculated for the recruiting user (second user) according to the analysis conditions of the applying user (first user), and a score is calculated for the recruiting user (first user) as well. A score is calculated according to the analysis conditions of user 2). As this analysis method, the method used in the first embodiment can be used.

Then, in step S14 of FIG. 4, the output unit 35 of the server device 30 outputs information on the other party according to the analysis result by the analysis unit 34 to the user terminals 10, 20 via the communication network 2 (step S14). . Thereby, the recruiting user (second user) and the applying user (first user) can learn about each other's ratings and the like. In other words, as in the first embodiment, the applicant user (first user) can refer to his or her own rating to determine the degree of compatibility of his or her own attributes with the recruiting user (second user). can. Further, the recruiting user (second user) can judge the degree of compatibility of his/her own attributes with the applying user (first user) by referring to his/her own rating and the like.

According to the second embodiment described above, the analysis for each user is performed in consideration of the difference between the future desired by the recruiting user (second user) and the applying user (first user) and the current state of each user. It becomes possible to do so. As a result, the applicant user (first user) can know the recruiting user (second user) who is compatible with his/her own attributes, and also the direction of the human resources desired by the recruiting user (second user). It is possible to know this in advance. Furthermore, the recruiting user (second user) can know the applicant users (first user) who are compatible with his or her own attributes, and can also know the direction of the company desired by the applicant user (first user). It becomes possible to know.

[Modified example]
The invention is not limited to the embodiments described above. The embodiment described above may be modified as follows. Furthermore, two or more of the following modifications may be implemented in combination.
[Modification 1]
For example, in the first embodiment, the applicant user may input information (hereinafter referred to as input information) that is likely to be a special appeal point to the recruiting user. This input information may be any information specified by the applicant user from among the information included in the current information and future information, or may be input by the applicant user separately from the current information and future information. . The server device 30 uses this input information to analyze the applicant user. Specifically, as illustrated in FIG. 11, the server device 30 includes an input information acquisition unit 36 that acquires input information input by the user, and the analysis unit 34 stores information generated by the profile generation unit 32 for each user. Analysis is performed by comparing the profile and input information acquired by the input information acquisition unit 36 with the analysis conditions acquired by the analysis condition acquisition unit 33. In this analysis, for example, when comparing the input information acquired by the input information acquisition unit 36 with analysis conditions, a method may be considered in which the input information is evaluated by being weighted compared to the profile. For example, when the degree of compatibility between the input information and the analysis conditions is a certain value, the analysis unit 34 evaluates it higher than when the degree of compatibility between the profile and the analysis conditions is also a certain value (i.e. (considering that the degree of fitness is higher). This makes it possible for the applying user to show his/her maximum appeal point, and to know the recruiting users who are particularly suited to the appeal point. Note that this modification 1 can also be applied to the example of the second embodiment.

[Modification 2]
Furthermore, in the first embodiment, the applicant user may be prompted to input user attribute information that is assumed to be highly compatible with the analysis conditions specified by the recruiting user. Specifically, as illustrated in FIG. 11, the server device 30 compares the user attribute information of the applicant user with the analysis conditions specified by the recruiting user, and determines the content of the input information to be input by the applicant user. An input content suggestion unit 37 may be provided to suggest the input content. For example, if one of the analysis conditions is ``person who wishes to edit'', an example may be proposed such as ``If you are interested in editing work, it would be a good idea to express your interest.'' A large number of combinations of such correspondences between analysis conditions and proposal contents are stored in a database in advance in the input content proposal unit 37. This makes it possible for applying users to indicate their maximum appeal points that are likely to match the analysis conditions specified by the recruiting users, and furthermore, to know the recruiting users who match those appeal points. becomes. Note that this modification 2 can also be applied to the example of the second embodiment.

[Modification 3]
In the second embodiment, the analysis conditions of the recruiting user and the applying user may be presented to the other party. In this case, the output unit 35 outputs the analysis conditions specified by the second user to the first user, and outputs the analysis conditions specified by the first user to the second user. In this way, it becomes possible to know each other's analysis conditions.

[Modification 4]
A user's current information or future information can change significantly when various life events occur, such as admission to school, study abroad, graduation, passing an exam, finding a job, retiring, getting married, giving birth, starting a business, going public, etc. Therefore, when a predetermined life event occurs in a user, the user attribute information acquisition unit 31 acquires the user attribute information of the user, the profile generation unit 32 generates the user's profile, and the analysis unit 34 It is also possible to perform an analysis for. The timing at which a user's life event occurs can be specified by a method such as input by the user into the information processing system 1 or the server device 30. As a result, the analysis results including the score etc. are updated at a time when the user's current information or future information may change significantly. This modification 4 can also be applied to the examples of the first and second embodiments.

[Modification 5]
In the first embodiment and the second embodiment, in the relationship between a recruiting user (second user) and an applying user (first user), a user who performs some kind of recruitment and a user who applies for the recruitment. Although the above-mentioned analysis was performed, the first user and second user in the present invention are not limited to such application users and recruitment users. For example, the above-mentioned analysis may be performed on the relationship between a supply user who supplies some product or service and a demand user who demands the product or service.

Therefore, the usage scene to which the present invention is applied is not limited to the employment scene illustrated in the first embodiment or the job change scene illustrated in the second embodiment. Suitable usage scenarios for the first embodiment include, for example, offering products or services to users who wish to shop, searching for unknown items or friends, recruiting club activities, suggesting educational institutions or cram schools that suit the user, and liking the user. discovery of restaurants, suggestions for preferred financial products and travel products, suggestions for lifestyle and hobbies, provision of useful information before and after engagement and marriage, provision of useful products and service information during pregnancy and childbirth, and conditions. Possible options include, but are not limited to, selecting candidates for moving destination areas. In addition, suitable usage scenes for the second embodiment include searching for a marriage or romantic partner, selecting candidates for admission, matching people who are particular about their preferences with compatible people, headhunting for managers who are worried about finding a successor, and special skills. Possible options include, but are not limited to, requesting a side job that takes advantage of this, and re-discovering human resources suitable for an industry with a labor shortage.

[Modification 6]
In the first and second embodiments described above, the profile generation unit 32 generates a profile for each user using at least the difference between current information and future information included in the user attribute information, and the analysis unit 34 generates a profile for each user using at least the difference between current information and future information included in the user attribute information. The user's profile was compared with the acquired analysis conditions to analyze the user. However, generation of a profile is not necessarily essential, and the analysis unit 34 compares the difference between the current information and future information included in the user attribute information with the analysis conditions acquired for the user corresponding to the user attribute information. , the user may be analyzed. Specifically, the analysis unit 34 calculates the difference between the current information and future information included in the user attribute information and a score that is the degree or percentage of compliance with the analysis conditions, and calculates the score from a large number of applying users. A predetermined number of applying users are extracted in descending order of their scores, those applying users whose scores are equal to or higher than a threshold are selected, or they are classified into applying users whose scores are at the same level. As a method for the analysis unit 34 to calculate the score, the analysis unit 34 calculates a large number of sets of the difference (explanatory variable) of current information and future information prepared in advance, the analysis condition (explanatory variable), and the score (objective variable), for example. A learning model is generated by performing supervised machine learning using the data as training data, and the difference between current information and future information included in the user attribute information of the target application user and the analysis conditions of the target recruitment user are used as explanatory variables. One possible method is to input it into the learning model and obtain a score as the objective variable.

[Other variations]
It should be noted that the block diagram used to explain the above embodiment shows blocks in functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using one physically or logically coupled device, or may be realized using two or more physically or logically separated devices directly or indirectly (e.g. , wired, wireless, etc.) and may be realized using a plurality of these devices. The functional block may be realized by combining software with the one device or the plurality of devices.

Functions include judgment, decision, judgment, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, These include, but are not limited to, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assigning. I can't. For example, a functional block (configuration unit) that performs transmission is called a transmitting unit or a transmitter. In either case, as described above, the implementation method is not particularly limited.

For example, a server device in an embodiment of the present disclosure may function as a computer that performs processing of the present disclosure.

Each aspect/embodiment described in this disclosure applies to LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), and 5G (5th generation mobile communication system). system), FRA (Future Radio Access), NR (new Radio), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark) )), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, UWB (Ultra-WideBand), Bluetooth (registered trademark), and systems that utilize and are extended based on these. It may be applied to at least one next generation system. Furthermore, a combination of a plurality of systems may be applied (for example, a combination of at least one of LTE and LTE-A and 5G).

The order of the processing procedures, sequences, flowcharts, etc. of each aspect/embodiment described in this disclosure may be changed as long as there is no contradiction. For example, the methods described in this disclosure use an example order to present elements of the various steps and are not limited to the particular order presented.

The input/output information may be stored in a specific location (eg, memory) or may be managed using a management table. Information etc. to be input/output may be overwritten, updated, or additionally written. The output information etc. may be deleted. The input information etc. may be transmitted to other devices.

Judgment may be made using a value expressed by 1 bit (0 or 1), a truth value (Boolean: true or false), or a comparison of numerical values (for example, a predetermined value). (comparison with a value).

Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described in the present disclosure. The present disclosure can be implemented as modifications and variations without departing from the spirit and scope of the present disclosure as determined by the claims. Therefore, the description of the present disclosure is for the purpose of illustrative explanation and is not intended to have any limiting meaning on the present disclosure.

Software includes instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, whether referred to as software, firmware, middleware, microcode, hardware description language, or by any other name. , should be broadly construed to mean an application, software application, software package, routine, subroutine, object, executable, thread of execution, procedure, function, etc. Additionally, software, instructions, information, etc. may be sent and received via a transmission medium. For example, if the software uses wired technology (coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), etc.) and/or wireless technology (infrared, microwave, etc.) to When transmitted from a server or other remote source, these wired and/or wireless technologies are included within the definition of transmission medium.

The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc., which may be referred to throughout the above description, may refer to voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, light fields or photons, or any of these. It may also be represented by a combination of
Note that terms explained in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings.

In addition, the information, parameters, etc. described in this disclosure may be expressed using absolute values, relative values from a predetermined value, or using other corresponding information. may be expressed.

As used in this disclosure, the phrase "based on" does not mean "based solely on" unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

As used in this disclosure, any reference to elements using the designations "first," "second," etc. does not generally limit the amount or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Thus, reference to a first and second element does not imply that only two elements may be employed or that the first element must precede the second element in any way.

The "unit" in the configuration of each device described above may be replaced with "means", "circuit", "device", etc.

Where "include", "including" and variations thereof are used in this disclosure, these terms, like the term "comprising," are inclusive. It is intended that Furthermore, the term "or" as used in this disclosure is not intended to be exclusive or.

In this disclosure, when articles are added by translation, such as a, an, and the in English, the present disclosure may include that the nouns following these articles are plural.

DESCRIPTION OF SYMBOLS 1... Information processing system, 2... Communication network, 10... User terminal, 20... User terminal, 30... Server device, 31... User attribute information acquisition part, 32... Profile generation part, 33... Analysis condition acquisition part, 34... Analysis unit, 35...output unit, 3001...processor, 3002...memory, 3003...storage, 3004...communication device, 3005...input device, 3006...output device.

Claims (5)

Comprising a processor, a memory, and a communication device,
The processor controls reading and writing of data in the memory and communication by the communication device.
Current information, which is attribute information that each first user who wishes to get a job or change jobs has or has at the present time, and what the first user has as his or her hopes, desires, or ideals in the future. obtaining user attribute information including at least future information as information from a first user terminal used by the first user ;
A second user who is different from the first user and who is recruiting for employment or job change uses analysis conditions for at least one of user extraction, selection, classification, and evaluation. obtained from a second user terminal ;
generating a profile of each of the first users using at least the difference between the current information and the future information included in the acquired user attribute information;
The profile of the first user generated using the user attribute information is compared with the analysis condition acquired for the first user corresponding to the user attribute information, and the profile of the first user is determined. Perform at least one analysis of extraction, selection, classification, or evaluation of the first user based on the degree of compatibility between the first user and the analysis condition, and provide information according to the result of the analysis to the first user. output to the terminal and the second user terminal;
moreover,
When generating the profile, the processor:
(1) If the acquired current information and future information are evaluated using a common evaluation standard, the difference in evaluation is calculated as the difference between the current information and future information, and the first user generate a profile, or
(2) Find the difference in vectors when the result of performing natural language semantic analysis on the acquired current information and the future information and develop it into a vector space model as the difference between the current information and the future information. generating a profile for the first user; or
(3) Using the acquired current information and future information in a machine learning model with the difference between the current information and the future information as an explanatory variable and the profile as an objective variable, the above (1) or (2) is applied. generate a profile of the first user by inputting the difference obtained in
When determining the degree of fitness, the processor:
Create a machine learning model using a pre-prepared user profile and analysis conditions for at least one of user extraction, selection, classification, or evaluation as explanatory variables and using a pre-prepared degree of fitness as an objective variable. On the other hand, the degree of suitability is obtained by inputting the generated profile of the first user and the obtained analysis conditions.
An information processing device characterized by: The processor controls reading and writing of data in the memory and communication by the communication device.
A user that includes at least current information that is attribute information that the second user is currently experiencing or has, and future information that is information that the second user has as hopes, desires, or ideals in the future. acquiring attribute information from the second user terminal;
generating a profile of each of the second users using at least the difference between the current information and the future information included in the acquired user attribute information of the second users;
Obtaining analysis conditions specified by the first user for performing at least one of user extraction, selection, classification, or evaluation from the first user terminal ;
The second user's profile generated using the user attribute information of the second user and the analysis condition specified by the first user are compared, and the relationship between the profile and the analysis condition is determined. Based on the suitability, at least one of extraction, selection, classification, and evaluation is performed on the second user, and information according to the result of the analysis is transmitted to the first user terminal and the second user terminal. Output to user terminal,
moreover,
When generating the second user's profile, the processor:
(4) If the acquired current information and future information are evaluated using a common evaluation standard, the difference in evaluation is calculated as the difference between the current information and future information, and the second user's generate a profile, or
(5) Calculate the vector difference when the result of performing natural language semantic analysis on the acquired current information and the future information and develop it into a vector space model as the difference between the current information and the future information. generating a profile for the second user; or
(6) Using the acquired current information and future information in a machine learning model with the difference between the current information and the future information as an explanatory variable and the profile as an objective variable, the method described in (4) or (5) generate a profile of the second user by inputting the difference obtained in
When determining the degree of fitness, the processor:
For a machine learning model that uses the profile prepared in advance and analysis conditions for at least one of user extraction, selection, classification, or evaluation as explanatory variables, and uses a pre-prepared degree of fitness as an objective variable. , input the generated profile of the second user and the obtained analysis conditions to obtain the degree of suitability.
The information processing device according to claim 1, characterized in that: The processor controls reading and writing of data in the memory and communication by the communication device.
Outputting proposal contents corresponding to the acquired analysis conditions based on the correspondence between the analysis conditions stored in a memory and proposal contents that propose contents of input information to be input to the first user. The information processing device according to claim 1, characterized in that: The processor controls reading and writing of data in the memory and communication by the communication device.
The analysis conditions specified by the second user are output to the first user terminal of the first user, and the analysis conditions specified by the first user are output to the second user terminal of the second user. The information processing device according to claim 1 , wherein the information processing device outputs the information to a user terminal . The processor controls reading and writing of data in the memory and communication by the communication device.
When a life event of the first user occurs, the user attribute information of the first user is acquired, a profile of the first user is generated, and a user is extracted, sorted, classified, or evaluated. The information processing device according to claim 1, wherein the information processing device performs at least one of the following.

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