JP7418054B1 - Information processing device and computer program - Google Patents

Abstract

[Problem] To provide a new platform technology for the community. An information processing device 1 provides a dedicated platform to a plurality of communities where owners and members gather on the Internet NW. This information processing device 1 acquires the target content and the time-series behavior contents of owners and members based on the behavior policy of each community, analyzes the behavior policy and the behavior content, and personalizes each community. Furthermore, based on the analysis results, predicted interest in the target content is analyzed for each community. Then, one or more communities are selected according to the results of the analysis, the target content is presented to the selected one or more communities, and changes in the behavior of the community due to the presentation are qualitatively or Evaluate quantitatively. [Selection diagram] Figure 1

Description

The present invention relates to an information processing device and a computer program that provide a platform to a community on a computer network, for example.

In recent years, communities (online communities), which are places for interaction on computer networks, have often been utilized for business purposes. For example, Patent Document 1 discloses a technique for utilizing a community for marketing. This technology focuses on users whose behavior has changed in the community, and extracts comment information of other users to whom the user has responded in the community, thereby providing comment information that may affect the user's behavior due to some factor. The content is extracted and analyzed for use in marketing.

Patent Document 2 discloses that user action information indicating actions taken by a plurality of users using a community toward visiting a target store is acquired, and based on this user action information, each of a plurality of waitlist store users is A technique has been disclosed that estimates the degree of preference in a community, and the lower the degree of preference, the more likely the user will be selected as a target user to receive information on changing reservations to any reservation-enabled store.

Patent Document 3 discloses a technology that acquires activity information of each community, extracts community characteristics from the acquired activity information, and recommends at least one category to the target community based on the community characteristics. has been done.

Patent No. 5815168 JP2022-80621A JP2022-87835A

With the diversification of consumer lifestyles due to the spread of network technology, changes in market needs for business items have become noticeable. Additionally, as digital technology evolves, new market needs or new markets are often born that did not exist before. Therefore, with the community utilization form based on analysis results of previous facts, etc. disclosed in Patent Documents 1 to 3, it has become difficult to accurately grasp constantly changing market needs, and improvements have been required.

One of the objects of the present invention is to provide a community platform technology that facilitates marketing that flexibly responds to changing market needs for business items. Other objects of the present invention will become apparent from this disclosure.

One aspect of the present invention is an information processing device that provides a dedicated platform to a plurality of communities, each of which has owners and members gathered on a computer network. an acquisition means for acquiring the chronological behavior contents of the owner and the members; an analysis means for analyzing the action policy and the action contents and individualizing the analysis results for each community; an analysis means for analyzing predicted interest values for the target content for each community, selecting one or more of the communities according to the results of the analysis, and presenting the target content to the selected one or more of the communities; and evaluation means for qualitatively or quantitatively evaluating changes in the behavior of the community caused by the presentation. This information processing device can be realized, for example, by a computer having a communication function reading and executing a computer program.

According to the above aspect of the invention, it is possible to provide a community platform technology that facilitates marketing that flexibly responds to the fluidization of market needs for products and services.

1 is an overall configuration diagram of an information processing device according to an embodiment. FIG. 3 is an exemplary diagram of grouping of owners with similar attributes; FIG. 2 is an exemplary diagram of a community on the Internet as seen from an information processing device. FIG. 3 is an exemplary diagram of a top screen that is unique to a certain registered community. FIG. 3 is an explanatory diagram of the procedure of hypothesis processing by the information processing device. It is a detailed explanatory diagram of zero-order hypothesis processing. It is a detailed explanatory diagram of primary hypothesis processing. It is a detailed explanatory diagram of secondary hypothesis processing. It is a detailed explanatory diagram of tertiary hypothesis processing. It is a detailed explanatory diagram of 4th hypothesis processing.

Below, an example of an embodiment in which the present invention is applied to an information processing device that provides a platform to multiple communities (online communities) where owners and members with common attributes and interests gather on a computer network will be described. explain. A community is, for example, a group of members gathered by the owner or under the influence of the owner (region, ideas, events, values) or a collection of their attributes.

The information processing apparatus of this embodiment facilitates support for, for example, POC (Proof of Concept) processing, test marketing processing, fan marketing processing, etc. through the community.
POC is based on various hypothetical processes regarding whether or not a business item is a product (including a product or its parts, a product group, a group of parts), a service (including intangible services), etc. It involves the process of verifying the results of the hypothesis processing in a community environment that is as close as possible to the actual market environment, and evaluating the verification results from various perspectives.
Test marketing processing involves asking consumers to use a new product or service before it is released, and analyzing their reactions. Fan marketing processing is marketing processing for increasing the number of fans of products and services.

[overall structure]
FIG. 1 is an explanatory diagram of a computer network environment including an example of the functional configuration of an information processing device 1 according to the present embodiment. The information processing device 1 is a computer with a communication function that is connected to the Internet NW, which is an example of a computer network, and includes a CPU (Central Processing Unit), a ROM (Read only memory), a RAM (Random Access Memory), and an SSD ( The computer unit has storage such as a solid state drive (Solid State Drive) and a computer unit with these input/output interfaces. The present invention can be implemented by this computer unit executing the computer program of the present invention.

A plurality of communities have been built on the Internet NW. Therefore, a plurality of member terminals MT and a plurality of owner terminals OT are connected to the Internet NW. The member terminal MT is a communication terminal operated by a member, and the owner terminal OT is a communication terminal operated by an owner. Both are information terminals with communication functions that include the above-mentioned computer unit.

The information processing device 1 has a CPU that reads and executes the computer program of the present invention, thereby controlling the above-mentioned computer units such as a communication control section 11, a registration section 12, an information acquisition section 13, an analysis section 14, an analysis section 15, and a management section. It operates as a section 16, an evaluation section 17, an information presentation section 18, and a main control section 19. Additionally, a community DB 21, owner DB 22, member DB 23, content DB 24, and POC information DB 25 are constructed in the storage.

The communication control unit 11 controls communication with the Internet NW. The registration unit 12 registers a community built on the Internet NW and published content in the community DB 21. A registered community may be called a registered community. The registration unit 12 also stores the target content to be presented to each community, for example, the content of products or services that are candidate business items, or the content of combinations of products and services and conditions for providing them, in the content DB 24. It is registered in a manner that allows its contents to be changed.

The registration unit 12 also performs owner registration processing and member registration processing.
The owner registration process accepts the establishment of a community, and also collects the action policy of the community (purpose, direction, shortcomings, value you want to give to the community, etc.), the owner's attributes, and information from the owner. Behavior history such as outgoing calls and the like are registered in the owner DB 22 together with the owner's e-mail address in association with the identification information of the community.

From the viewpoint of personal information protection, it is desirable that the owner attributes are pseudo-personality attributes that imitate real human attributes (date of birth, blood type, gender, etc.) on a one-to-one basis, excluding the individual's name. The pseudo personality attributes are determined based on, for example, fortune-telling information based on human attributes. This makes it possible to predict the behavior of owners in individual communities without knowing owner attributes in detail.

In the member registration process, member attributes, which are attributes of members participating in the community, are registered for each community together with the member's e-mail address and request information requested by the owner that was input when participating in the community. For each community, it means to associate it with the identification information of the community. The requested information is, for example, a question from the owner, favorite food, favorite animal, etc.

The member DB 23 also registers the behavior history of members in the community. It is desirable that this member attribute is also the above-mentioned pseudo personality attribute. This makes it possible to predict the behavior of the member without knowing the member's attributes in detail.

Note that one owner may host one community while participating as a member in another community. In this case, the owner will be registered in both the owner DB 22 and member DB 23.

The information acquisition unit 13 acquires various information from each owner terminal OT, each member terminal MT, the registered community and other websites, and each DB 21 to 24. For example, the target content is acquired from a request terminal (not shown) or an owner terminal OT connected to the Internet NW, or from the POC information DB 25. One of the target contents used for POC is, for example, one or more POC hypotheses created or modified to predict the feasibility of a business item candidate, and POC results that reflect the results.
The information acquisition unit 13 also acquires time-series behavior details of owners and members based on the behavior policy of each community from the owner terminal OT, member terminal MT, owner DB 22, or member DB 23.

The analysis unit 14 analyzes the content of the community. For example, by analyzing the behavioral policy, owner attributes, member attributes, and behavioral details of owners and members obtained in chronological order for each community, and depending on the analysis results, groups, subdivisions, and hierarchies of communities, owners, and members can be performed. I do. Moreover, when grouping, each group is individualized and each group is additionally registered in the community DB 21 as a new community.
Note that "individualization" refers to, for example, classifying into categories that represent the culture and values unique to the community. The category is one of the identification information of the community, etc., and is preferably a quantitative value, but may also be a qualitative word or phrase.

FIG. 2 is an exemplary diagram of grouping of communities and the like. Here, we focus on owner attributes, and out of the multiple communities that actually exist, there is a group G10 to which multiple owners whose "income" exceeds a predetermined standard are similar, and a group G10 where "hobbies and preferences" exceed a predetermined standard. Another similar group G20 is shown. In addition, owners who are similar in terms of "gender," "age," "organization," "ideology," etc. beyond a predetermined standard may be grouped together. The same applies to communities or members. Segmentation is, for example, separating members in one community who have the same or similar "income", "hobbies and preferences", "gender", "age", "organization", "ideology", etc. In other words, hierarchization means, for example, grouping or subdividing communities into hierarchies for each genre.

Due to grouping, subdivision, hierarchization, etc., the community existing on the Internet NW as seen from the information processing device 1 is illustrated in FIG. 3. That is, in the illustrated example, the grouped "PTA" community G30, "study group" community G40, "company A" community G50, "dojo" community G60 for judo, etc., are subdivided from multiple communities, and are further divided into hierarchies. Hierarchical G#1, Hierarchical G#2, etc. are shown.

In addition to the action policy for each community, the analysis unit 14 acquires and analyzes the notification rate to members of the information sent by the owner in the community and/or the reaction content of the members to the sent information from, for example, the community DB 22. However, the community may be individualized based on the results of this analysis. The notification rate can be calculated, for example, by the ratio of the number of members' responses to the number of calls made by the owner, or the number of members who responded to one call made by the owner.

The analysis unit 14 analyzes combinations of relationship information representing owner attributes, member attributes, owner attributes and member attributes, and personal relationships between owners and members, in addition to the action policy of each community and the action content of each community. At least one of them may be analyzed and the community may be individualized based on the results of this analysis.
Alternatively, the analysis unit 14 may be configured to analyze market needs based on market conditions of various products etc. acquired from corresponding websites on the Internet NW.

The analysis unit 15 analyzes the times of interest and trends of members of each community. For example, based on the results of the analysis by the analysis unit 14, the predicted interest value for the target content when the target content is presented is analyzed for each community. The predicted interest value is, for example, a value between 1% and 99%, and is obtained by machine learning statistical values such as behavior history acquired from many community sites and websites existing on the Internet NW, owner DB 22, and member DB 23. It is derived by

The management section 16 will be explained. The management unit 16 mainly performs community management, owner management, member management, content management, and hypothesis management.

The management unit 16 performs community management by searching for communities that meet predetermined conditions among the many communities that exist on the Internet NW in addition to registered communities, and checking the owner attributes of the owners of the communities identified through the search and the membership of the communities. Identify member attributes. Further, the specified owner attribute is additionally registered in the owner DB 22, and the specified member attribute is additionally registered in the member DB 23. Furthermore, the analysis unit 15 generates a new grouped, subdivided, and hierarchical community, and additionally registers this in the community DB 21. In addition, the information of registered or additionally registered communities may be modified or deleted as appropriate.

Owner management performed by the management unit 16 includes, for example, registration of owner attributes for each community in the owner DB 22, additional registration, modification, deletion, and association of member attributes of the same person and communities. Another form of owner management is to identify owners with similar attributes based on owner attributes and to associate the identified owner attributes with each other. The similarity of owner attributes can be determined by using, for example, a collaborative filtering algorithm. In addition to collaborative filtering, known clustering algorithms and machine learning algorithms can also be used.

Member management performed by the management unit 16 includes registration of member attributes for each community in the member DB 22, additional registration, modification, deletion, and association of owner attributes of the same person and communities. Another form of member management is to identify members with similar attributes based on member attributes and to associate the identified member attributes with each other. The same method as for determining the similarity of owner attributes can be used to determine the similarity of member attributes.

The content management performed by the management unit 16 includes, for example, generating unique content or original applications whose predicted interest value is expected to be a certain value or more based on the behavior history of registered community members, or They are acquired from the terminal OT and registered in the content DB 24, and edits and deletions are accepted as appropriate.
One of the unique contents is a video, still image, application, or URL of a website where these are stored, which are linked to the top screen or a lower layer screen thereof, which is unique to the community. The above target content is also one of the unique contents.

FIG. 4 is an exemplary diagram of unique content. In the example of FIG. 4, a top screen unique to a certain registered community is shown. In the illustrated example, in addition to "Notices" 201, 202 and "Notices from Community Owners" 203 for all registered communities, a content group 200 that can be viewed or used only by the owners and members of the registered community is posted. .

Hypothesis management performed by the management unit 16 generates hypotheses for test marketing, fan marketing, and POC of target content. The hypothesis is a tentative answer that is considered to be the most probable, and can be generated using the analysis results of the analysis unit 14 and/or the analysis results of the analysis unit 15. For example, when the predicted interest value for a certain product is low, one of the hypotheses may be "the price is too high" or "there are too many unnecessary functions".
Hypotheses include "current hypothesis" and "execution hypothesis." A current hypothesis is a hypothesis based on speculation about the current situation, such as ``Isn't this the way things are now?'' A current hypothesis has the advantage of making it clear who to ask what. On the other hand, an implementation hypothesis is a strategic hypothesis based on an implementation plan that says, ``If the current hypothesis is such, then if we adopt this kind of strategy, it will work.''

The evaluation section 17 will be explained. The evaluation unit 17 performs various evaluations depending on the purpose of each community. Information for evaluation can be obtained from, for example, the community DB 21, the owner DB 22, the member DB 23, the content DB 24, and/or the analysis result of the analysis section 15 obtained through the management section 16 can be used.

In one aspect, the evaluation unit 17 verifies the above hypothesis. Further, in one embodiment, the evaluation unit 17 selects one or more communities to which the target content is to be introduced, according to the result of the analysis by the analysis unit 16, and the evaluation unit 17 selects one or more communities to which the target content is to be input, and the evaluation unit 17 selects the target content for the selected one or more communities. At the same time, the changes in the behavior of the community due to the presentation are qualitatively or quantitatively evaluated. The selection of one or more communities to be presented is performed, for example, by identifying one or more communities with relatively high predicted interest values. Alternatively, this can be done by identifying all communities whose predicted interest in the target content exceeds a certain value.

The information presentation unit 18 presents presentation content to the community selected by the evaluation unit 17 through the communication control unit 11.
The main control unit 19 centrally controls the operations of the communication control unit 11, registration unit 12, information acquisition unit 13, analysis unit 14, analysis unit 15, management unit 16, evaluation unit 17, and information presentation unit 18 described above. .

[Operation form]
Next, an example will be described in which the information processing device 1 configured as described above is operated as a POC platform. Here, as shown in FIG. 5, the information processing device 1 performs zero-order hypothesis processing S0, first-order hypothesis processing S1, second-order hypothesis processing S2, third-order hypothesis processing S3, fourth-order hypothesis processing S4, and POC processing S5. An example of processing will be explained.

FIG. 6 is a detailed procedure explanatory diagram of the zero-order hypothesis processing (S0) executed by the information processing device 1.
In the zero-order hypothesis processing, current hypothesis processing is performed for POC preparation. The information processing device 1 first receives an input of information about a set rental of a plurality of products that are business item candidates as an example of target content (S01). Then, target content analysis (S02) and community analysis (S03) are performed. In this example, it is assumed that the target content analysis is the contents of a plurality of products and the offer price of a set rental. Community analysis is a process of searching for a community where topics related to product set rental are discussed, for example.

If such a community exists, the information processing device 1 determines a target customer (anticipated customer) for product set rental (S04). Target customers include purchasers, prospective purchasers, purchasers, etc. When the target customer is determined, the information processing device 1 selects a community of interest, that is, a community to which content (catalogs, proposals, etc.) related to product set rental will be presented, and presents the content (S05). The information processing device 1 detects the degree of community reaction (S06), and stores (registers) the detection result in the POC information DB 25 (S07). The degree of community response includes, for example, the number of statements made by the owner and members, the contents of the statements (positive/negative), the reaction rate of members to the owner's statements (positive/negative/ignored), and the above interest in relation to a predetermined standard value. It can be detected by the ratio of predicted values.

FIG. 7 is a detailed procedure explanatory diagram of the primary hypothesis processing (S1) executed by the information processing device 1.
In the primary hypothesis processing, hypothesis processing is performed that focuses on the owner's attributes, taking advantage of the law that they tend to greatly influence the consciousness and behavior of each member of the community.
The information processing device 1 first analyzes the detection results of the zero-order hypothesis processing (S11). Depending on the analysis results, the conditions for providing product set rentals will be changed. Next, the owner attribute of the owner who leads the presentation destination community is acquired (S12), and based on the owner attribute, it is determined whether the owner's behavior regarding product set rental has been predicted (S13). If the behavior has been predicted (S13: Y), the owner is registered as a persona for product set rental (S14). A "persona" is a virtual personality with a unique personality, created based on various purchasing activities and behavior history. In this embodiment, a persona is used as a virtual personality for purchase prediction and preference prediction.

The information processing device 1 estimates the purchaser of the product set rental based on the behavior history of the owner serving as the persona (S15), and transmits the market needs regarding the product set rental to the corresponding website on the Internet NW, for example. It is detected by accessing it (S17). Furthermore, other communities to which owners with attributes similar to those of the owner belong are detected as similar communities (S18). Information on similar communities can be used to detect communities or similar market needs for use in the next hypothesis process. In S13, if the owner's behavior is not predicted (S13: N), the behavior information is obtained by a separate method such as interviewing the owner via e-mail, and prediction processing is performed (S16). The process moves to detection of market needs (S17). The information processing device 1 stores (registers) the results of the primary hypothesis processing in the POC information DB 25 (S19).

FIG. 8 is a detailed procedure explanatory diagram of the secondary hypothesis processing (S2) executed by the information processing device 1.
In the secondary hypothesis processing, hypothesis processing is performed focusing on the attributes of each member of the community. That is, the information processing device 1 first analyzes the results of the primary hypothesis processing, and if a new issue is discovered, changes the conditions for providing the product set rental, which is the target content (S21). Further, the member attributes of the members of the community to which the product set rental information is presented are acquired (S22), and it is determined whether the behavior toward the target content has been predicted based on the member attributes (S23). If the behavior has been predicted (S23: Y), the member is registered as a persona for product set rental (S24). Persona is as described above.

The purchaser of the product set rental is estimated based on the behavior history of the member serving as the persona (S25), and the market needs for the product set rental are detected (S27). Further, similar members having attributes similar to the member attributes of the member and other communities to which the similar members belong are detected as similar communities (S28). Information on similar members can be used to provide a platform for new communities or to detect similar market needs.
In S23, if the member's behavior is not predicted (S23: N), the behavior information is obtained by a separate method, such as interviewing the member via email, and prediction processing based on the behavior information is performed. (S26), and based on the result, the process moves to market needs detection (S27). The information processing device 1 stores (registers) the results of the secondary hypothesis processing in the POC information DB 25 (S29).

FIG. 9 is a detailed procedure explanatory diagram of the tertiary hypothesis processing (S3) executed by the information processing device 1.
In the tertiary hypothesis processing, processing related to the first stage execution hypothesis is performed. That is, the information processing device 1 analyzes the results of the primary hypothesis processing and secondary hypothesis processing (S31). If new issues are discovered based on the results of the analysis, we will change the conditions for providing product set rentals.
Next, the information processing device 1 actually provides samples of the product to the owners and members registered as personas and asks them to actually use them, and learns about improvements in the provision format, possibility of purchase, and current situation. Prices, requests, etc. are collected through e-mail etc. (S33).

The information processing device 1 analyzes the collected contents and quantitatively or qualitatively evaluates the degree of satisfaction of the personas with respect to the product provided as a sample using the above-described method (S34). If the overall satisfaction level is lower than the assumed standard value (S34: N), the information processing device 1 provides another option corresponding to the provision conditions of the set rental when the set rental price, product combination, etc. are changed. Information indicating that the sample has been re-provided to the persona is obtained, and the process returns to S33 (S35). In S34, if the satisfaction level exceeds the assumed standard value (S34: Y), the information processing device 1 performs processing to increase the index representing the likelihood, that is, the certainty, of the market needs detected in the previous hypothesis processing. (S36), and further similar members are detected (S37). The information processing device 1 stores (registers) the results of the tertiary hypothesis processing in the POC information DB 25 (S38).

FIG. 10 is a detailed procedure explanatory diagram of the fourth hypothesis processing (S4) executed by the information processing device 1. In the fourth hypothesis processing, processing related to the second stage execution hypothesis is performed. That is, the information processing device 1 analyzes the results of the tertiary hypothesis processing (S41). If a new issue is discovered as a result of the analysis, candidate strategies for solving the issue are identified, for example, by machine learning that uses information on methods for solving the issues discovered so far as input. Furthermore, the human relationship bias of the owner and members of the community to which the persona identified in the tertiary hypothesis process belongs is detected (S42). The human relationship bias is a numerical value estimated based on, for example, the content (affirmation/denial/ignorance) of the owner's response to the member's statement.

The information processing device 1 determines conditions for providing the re-determined product sample or a different sample based on the human relationship bias (S43), and also determines the conditions for providing the re-determined product sample or a different sample (S43), and the information processing device 1 determines the conditions for providing the re-determined product sample or a different sample (S43), and also provides information to the owners of multiple communities including the community to which the persona belongs, and/or Samples with different provision conditions are simultaneously provided to members (S44), and responses from each community are analyzed and evaluated (S45). The information processing device 1 creates marketing or branding content tailored to the personality of the community that is satisfied or unsatisfied based on, for example, registered information in the POC information DB 25 and a predetermined format (S46), and creates this as a fourth hypothesis. The result of the processing is saved (registered) (S47).

As described above, the information processing device 1 of the present embodiment acquires the time-series behavior contents of owners and members based on the behavior policy of each community, analyzes the behavior policy and the behavior content, and sends the analysis results for each community. At the same time, based on the results of the analysis, predicted interest values for target content (for example, content related to business item candidates) are analyzed for each community. Then, one or more communities are selected according to the results of the analysis, the target content is presented to the selected one or more communities, and changes in the behavior of the community due to the presentation are qualitatively or Evaluate quantitatively. Therefore, it is possible to provide a community platform technology that facilitates marketing that flexibly responds to the fluidity of market needs for business items.

In the above operation mode, an example of facilitating POC has been described, but the same procedure can also be applied to test marketing processing or fan marketing processing. In addition, in the above operation mode, the target content is an example of content when the business item is product set rental, but other business items, such as food, clothing, daily necessities, financial products, funds, etc. The same can be applied to.

DESCRIPTION OF SYMBOLS 1... Information processing device, 11... Communication control unit, 12... Registration unit, 13... Information acquisition unit, 14... Analysis unit, 15... Analysis unit, 16... Management Section, 17...Evaluation section, 18...Information presentation section, 19...Main control section, 21...Community DB, 22...Owner DB, 23...Member DB, 24... Content DB, 25...POC information DB.

Claims (10)

An information processing device that provides a dedicated platform to multiple communities, each of which has its own owners and members, gathered on a computer network,
acquisition means for acquiring target content and chronological action details of the owner and the member based on the action policy of each community;
an analysis means for analyzing the action policy and the action content and individualizing the analysis results for each community;
analysis means for analyzing predicted interest values for the target content for each community based on the results of the analysis;
Selecting one or more of the communities according to the results of the analysis, presenting the target content to the selected one or more of the communities, and qualitatively determining changes in the behavior of the community due to the presentation. evaluation means for quantitative or quantitative evaluation;
An information processing device equipped with In addition to the action policy, the analysis means analyzes the notification rate to the members of the information transmitted by the owner in the community and/or the reaction content of the members to the transmitted information, and analyzes the results of this analysis. individualize the community based on;
The information processing device according to claim 1. In addition to the action policy and the action content, the analysis means includes an owner attribute that is an attribute of the owner, a member attribute that is an attribute of the member, the owner attribute and the member attribute, and the person of the owner and the member. 2. The information processing device according to claim 1, wherein the information processing device analyzes at least one of the combinations of relationship information representing a relationship, and personalizes the community based on the result of this analysis. The owner attribute, the member attribute, or the owner attribute and the member attribute are pseudo-personality attributes that imitate real human attributes on a one-to-one basis, each excluding a personal name;
The information processing device according to claim 3. the pseudo-personality attributes are determined based on fortune-telling information;
The information processing device according to claim 4. The analysis means associates the plurality of owners whose owner attributes are similar within a predetermined range, or the members whose member attributes are similar within a predetermined range, and stores them as one of the selection candidates.
The information processing device according to claim 3. Claims 1 to 5, wherein the target content is content related to one or more hypotheses created or modified to predict the feasibility of a business of goods, services, or a combination of goods and services as business item candidates. The information processing device according to any one of the above. The evaluation means selects one or more of the communities in which the predicted interest value is relatively high as a community to which the target content is presented.
The information processing device according to any one of claims 1 to 5. The evaluation means selects all the communities in which the predicted interest value exceeds a certain value as communities to which the target content is presented.
The information processing device according to any one of claims 1 to 5. A computer program that causes a computer having a communication function to operate as the information processing apparatus according to claim 1.

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