JP6417002B1 - Generating device, generating method, and generating program - Google Patents

Abstract

A generation device, a generation method, and a generation program for determining a trustworthiness from various viewpoints are provided.
A generation apparatus according to the present application includes an acquisition unit and a generation unit. The acquisition unit acquires information relating to the logistics of the product that is the object of the commercial transaction. A generation part produces | generates the model for calculating the creditworthiness of the target person of a commercial transaction based on the information regarding the physical distribution acquired by the acquisition part. For example, the acquisition unit acquires, as information relating to logistics, a return rate at which a product is returned after the provider ships the product. The generation unit generates a model for calculating the trustworthiness of the provider based on the return rate.
[Selection] Figure 1

Description

  The present invention relates to a generation device, a generation method, and a generation program.

  Conventionally, various commercial transactions are performed via the Internet. For example, on the Internet, a store that manages a store and a user, and a platform that mediates a commercial transaction between users are provided.

  As a technique related to the commercial transaction as described above, a technique for calculating the credit quality of a business operator based on the behavior of a user on a network is known.

JP 2006-118932 A

  However, there is room for further improvement in the method for obtaining the trust of the target person of the commercial transaction. For example, in commercial transactions, the completion of the actual delivery of goods, such as the smooth delivery of goods or the delivery of goods, can be an element for determining the trust of the person who is the object of the commercial transaction. However, in the prior art, the creditworthiness is calculated based on the user's behavior history etc. on the network, but it is mentioned that the creditworthiness is calculated from various viewpoints such as information related to the delivery of the actual product after the completion of the commercial transaction. Not.

  The present application has been made in view of the above, and an object thereof is to provide a generation device, a generation method, and a generation program that can determine the reliability from various viewpoints.

  The generation device according to the present application is for obtaining information relating to the logistics of a product that is the subject of a commercial transaction, and calculating the creditworthiness of the target person of the commercial transaction based on the information relating to the logistics acquired by the obtaining unit. And a generation unit for generating a model.

  According to one aspect of the embodiment, there is an effect that the reliability can be determined from various viewpoints.

FIG. 1 is a diagram illustrating an example of a generation process according to the embodiment. FIG. 2 is a diagram illustrating a configuration example of the generation system according to the embodiment. FIG. 3 is a diagram illustrating a configuration example of the generation apparatus according to the embodiment. FIG. 4 is a diagram illustrating an example of the store information storage unit according to the embodiment. FIG. 5 is a diagram illustrating an example of a user information storage unit according to the embodiment. FIG. 6 is a diagram illustrating an example of the store model storage unit according to the embodiment. FIG. 7 is a diagram illustrating an example of a user model storage unit according to the embodiment. FIG. 8 is a flowchart (1) illustrating a processing procedure performed by the generation apparatus according to the embodiment. FIG. 9 is a flowchart (2) illustrating a processing procedure performed by the generation apparatus according to the embodiment. FIG. 10 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the generation apparatus.

  Hereinafter, a mode for carrying out a generation device, a generation method, and a generation program according to the present application (hereinafter referred to as “embodiment”) will be described in detail with reference to the drawings. Note that the generation device, the generation method, and the generation program according to the present application are not limited by this embodiment. In the following embodiments, the same portions are denoted by the same reference numerals, and redundant description is omitted.

[1. Example of generation process)
First, an example of the generation process according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of a generation process according to the embodiment. In FIG. 1, an example of a generation process executed by the generation apparatus 100 according to the embodiment will be described. Specifically, in FIG. 1, the generation device 100 calculates the credit of a store that opens a predetermined commercial transaction site on the network and a user who uses the commercial transaction site based on information related to the distribution of goods. An example in which the process of generating the model (calculation formula) is performed will be described.

  In the example illustrated in FIG. 1, the generation device 100, the user terminal 10, the web server 30, and the delivery company device 50 are connected to be communicable via a communication network (for example, the Internet) not shown.

  A generation apparatus 100 illustrated in FIG. 1 is a server apparatus that generates a model for calculating a credit rating of a target person of a commercial transaction. The generation apparatus 100 generates a model for calculating the credit rating of a product provider (store) or a product purchaser (user), for example, as an object of a commercial transaction. In the embodiment, a store that opens a store in a shopping mall is given as an example of a product provider. However, the provider is not limited to this example, and may be, for example, a transaction between individuals or an exhibitor on an auction site. Good.

  A user terminal 10 illustrated in FIG. 1 is an information processing apparatus used by a user. Specifically, the user terminal 10 allows a user to browse a web page, purchases a product on a website (for example, a shopping mall on the network), or posts a product information evaluation (so-called review). Used for. In the example of FIG. 1, the user terminal 10 is, for example, a smartphone or a tablet terminal.

  The web server 30 illustrated in FIG. 1 is a server device that provides various web pages when accessed from the user terminal 10. In the example illustrated in FIG. 1, the web server 30 provides a shopping mall site on the network. In a shopping mall provided by the web server 30, each business operator opens a store that handles products of various categories.

  Further, the web server 30 may acquire information regarding user behavior on the network via a website to be provided. Information about user behavior is stored in, for example, a storage unit included in the web server 30 or a predetermined external storage device. Information regarding user behavior on the network refers to information transmitted from the user terminal 10 in accordance with the user's operation when using a service (shopping mall in the example of FIG. 1) provided from the web server 30, or user behavior in the service. It is the information which showed. For example, the user behavior on the network is a purchase behavior in a shopping mall, a posting of a review from a user for each store in the shopping mall, or the like. The web server 30 may acquire not only user behavior but also data such as a comment returned by the store manager in response to a review from the user.

  The delivery company device 50 is a server device used by the delivery company. In the example of FIG. 1, the delivery company apparatus 50 is used by a delivery company 40 that is an example of a delivery company. The delivery company apparatus 50 receives a request for shipping products from a store that opens in a shopping mall. Then, the delivery company apparatus 50 generates delivery information that is information in which the date and time of delivery and the delivery destination are registered. The delivery company 40 performs delivery to the delivery destination based on the delivery information generated by the delivery company device 50. Although illustration in FIG. 1 is omitted, a plurality of delivery companies 40 may exist. That is, the store may select one of the plurality of delivery companies 40 and request shipping of the product. In the following, the delivery company may be read as the delivery company device 50. For example, the description “the delivery company 40 transmits information” may actually indicate the situation “the delivery company device 50 used by the delivery company 40 transmits information”.

  In addition, the delivery company device 50 provides the generation device 100 with information related to physical distribution. Information related to physical distribution includes, for example, a target of a commercial transaction such as completion of delivery to the user, change of delivery date / time from the user, return of a product delivered to the user, etc. Contains various information related to the delivery of the product.

  By the way, in a commercial transaction site such as a shopping mall provided by the web server 30, it is desired to measure the credibility of a user or a store (or a store manager who manages the store) that is a target of the commercial transaction and perform smooth operation. There is a request. For example, in the case of a store, such a degree of trust is an index value based on sales of products, evaluation values from users, and the like. Further, if the user is a user, the degree of credit can be an index value based on the number of transactions, whether the purchase price is securely transferred, or the like. By calculating the credit rating, the web server 30 can, for example, refuse to open a store with a low credit rating or restrict the use of a user with a low credit rating, and thus can perform smooth operation.

  Here, in the commercial transaction, the completion of the delivery of the actual product, such as the smooth shipment of the product or the delivery of the product, can be an element for determining the trust of the target person of the commercial transaction. Therefore, the generation apparatus 100 according to the embodiment, for example, cooperates with the delivery company apparatus 50 and the like to acquire information related to the physical distribution of the target person of the commercial transaction and generate a model for calculating the credit rating based on the information regarding the physical distribution. Thereby, the production | generation apparatus 100 can calculate reliability based on the data according to the actual condition in a commercial transaction site, such as delivery and receipt of goods more. Hereinafter, the generation process according to the embodiment will be described along the flow with reference to FIG. 1.

  In the example illustrated in FIG. 1, a store A01 operated by the store manager 70 is open in a shopping mall provided by the web server 30. Although not shown in FIG. 1, the shopping mall provided by the web server 30 has a considerable number of stores enough to obtain a sufficient amount of information for performing the generation processing according to the embodiment. It shall be.

  The user terminal 10 accesses the web server 30 according to an operation by a user U01 that is an example of a user who uses a shopping mall. Although not shown in the example of FIG. 1, there are a plurality of users and user terminals 10, and a considerable number of accesses are obtained so that a sufficient amount of information can be obtained for performing the generation processing according to the embodiment. Suppose you are going. The user U01 who has accessed the shopping mall uses the shopping mall through behavior such as purchase behavior and review transmission (step S11).

  The web server 30 acquires various information transmitted from the user terminal 10. For example, the web server 30 acquires information indicating that the user U01 has purchased a predetermined product in the store A01. Further, the web server 30 may acquire information related to the evaluation from the user U01 for the store A01. The information related to the evaluation from the user U01 is, for example, text data posted as a review, a score for the store A01 (evaluation value for the store. For example, it is indicated by a numerical value of 5 levels).

  Further, the web server 30 may acquire information related to delivery that occurs with respect to an order placed by the user terminal 10. For example, the web server 30 acquires information that the store A01 that has received the order for the product from the user U01 has requested the delivery company device 50 to ship the product. Further, the web server 30 may acquire information indicating whether the store A01 has been able to make a request for shipping the product without any problem as information relating to logistics. For example, the web server 30 determines that the product ordered from the user U01 has not been out of stock in the store A01, or within a predetermined period from the order (for example, within a period from the start of the delivery procedure determined by the shopping mall. , Within 24 hours, etc.), the store A01 obtains information such as whether or not the goods have been shipped.

  Thereafter, when the store A01 makes a request for shipping the product to the delivery company device 50 (step S12), the delivery company 40 sends the product to the user U01 (step S13).

  The delivery company apparatus 50 acquires information related to the distribution of such products. For example, whether the delivery company device 50 is at home at the delivery date and time designated by the user U01 (in other words, whether the receipt of the product is completed by one delivery), or has there been an action such as frequently changing the delivery date and time? Get no. Further, for example, the delivery company device 50 may acquire information such as whether or not the delivery company 40 has received an unreasonable complaint during delivery to the user U01. The delivery company apparatus 50 acquires such information based on feedback from a terminal device owned by the delivery company 40, for example.

  In this way, in a shopping mall, orders for various stores are generated from a large number of users, and a large number of products are delivered.

  The generation apparatus 100 acquires information on the store and the user including information on the commercial transaction as described above and information on the physical distribution of the product in the commercial transaction from the web server 30 (step S14).

  In addition, the generation apparatus 100 acquires information related to physical distribution when the goods are actually delivered from the delivery company apparatus 50 (step S15).

  And the production | generation apparatus 100 produces | generates the model for calculating the creditworthiness of the target person of a commercial transaction based on the acquired information (step S16). For example, the generation apparatus 100 generates a model for calculating the credit rating of the store and a model for calculating the credit rating of the user.

  For example, the generation device 100 generates a model that outputs a predetermined index value (score) when information on a store or a user that is a target of credit calculation is input. Although details will be described later, for example, the generation apparatus 100 uses various information regarding logistics as features (determination factors), scores the information corresponding to each feature, and finally determines the object of credit calculation. A model that outputs the credit rating of the store or user as a score is generated.

  For example, the generation apparatus 100 assigns the lowest score to a store evaluation value without describing a user who frequently posts a term set as a negative word or a comment on a commercial transaction platform such as a shopping mall. For a user who takes such an action, a model may be generated such that the degree of trust is calculated to be relatively low. Alternatively, the generation device 100 generates a model in which the reliability is calculated to be relatively low for a user who makes an unreasonable claim to the delivery company 40 or a user who has specified the delivery date and time but has not been at home. May be.

  In addition, the generation apparatus 100 stores, for example, a store with a high return rate of purchased products, a store in which an out-of-stock item has been received, or a store from which information such as a stock out is not quickly resolved A model may be generated such that the degree of trust is calculated to be relatively low.

  As described above, the generating apparatus 100 according to the embodiment acquires information related to the physical distribution of the product that is the target of the commercial transaction, and generates a model that calculates the credibility of the target person of the commercial transaction based on the acquired information related to the physical distribution.

  In other words, the generating apparatus 100 according to the embodiment generates a model for calculating the credit quality of a store from the viewpoint of goods distribution as well as financial information and transaction information generally used for measuring the credit quality of the store. . The generation apparatus 100 also generates a model for calculating the user's reliability from the viewpoint of merchandise logistics as well as transaction information and behavior information generally used for measuring the user's reliability. . As described above, the generation apparatus 100 can generate a model based on information that only the delivery company 40 knows, as well as information related to actual product shipment, as well as information on a shopping mall, which is a commercial transaction platform. it can. Thereby, the production | generation apparatus 100 can determine the reliability of a store or a user from various viewpoints. In addition, the generation apparatus 100 calculates the degree of trust by acquiring information on the physical distribution of the user or the store in real time and inputting the information into the model. For this reason, the production | generation apparatus 100 can calculate the credit rating of a store or a user using data with high freshness. Hereinafter, the configuration of the generation apparatus 100 that performs the above-described processing and the generation system 1 including the generation apparatus 100 will be described in detail.

[2. Generation system configuration)
The configuration of the generation system 1 including the generation apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 2 is a diagram illustrating a configuration example of the generation system 1 according to the embodiment. As illustrated in FIG. 2, the generation system 1 according to the embodiment includes a user terminal 10, a store terminal 20, a web server 30, a delivery company device 50, and a generation device 100. These various apparatuses are communicably connected via a network N (for example, the Internet) in a wired or wireless manner. The number of devices included in the generation system 1 illustrated in FIG. 2 is not limited to the number illustrated. For example, the generation system 1 may include a plurality of user terminals 10 and the like.

  The user terminal 10 is, for example, an information processing apparatus such as a smartphone, a desktop PC (Personal Computer), a notebook PC, a tablet terminal, a mobile phone, a PDA (Personal Digital Assistant), or a wearable device. It is. The user terminal 10 obtains a web page (for example, the web page of the store A01 shown in FIG. 1) from the website provided from the web server 30 by accessing the web server 30 according to the operation by the user. Then, the user terminal 10 displays the acquired web page on a display device (for example, a liquid crystal display). In the present specification, the user and the user terminal 10 may be identified with each other. For example, “the user accesses the web server 30” may actually mean “the user terminal 10 used by the user accesses the web server 30”.

  The store terminal 20 is an information processing apparatus used by a business operator that manages the store (store manager 70 in the example of FIG. 1). Specifically, the store terminal 20 executes information processing related to store operation in a shopping mall, information processing for requesting a delivery company to ship a product when an order for the product is received from a user. .

  The web server 30 is a server device that provides various web pages when accessed from the user terminal 10. In the example of FIG. 1, an example is shown in which the web server 30 provides a website corresponding to a shopping mall, and provides a web page of a store A01 opening a store in the shopping mall. However, the web page provided by the web server 30 is not limited to this, and the web server 30 includes, for example, a news site, a weather forecast site, a shopping site, a finance (stock price) site, a route search site, a map providing site, a travel site, Various web pages related to restaurants introduction sites, web blogs, etc. may be provided.

  Further, the web server 30 may obtain information on each website and information on the network. The information on the network is information transmitted from the user terminal 10 in accordance with a user operation when using services provided from various websites, for example. For example, the information on the network includes information related to a search query in a search site, information related to purchase behavior in a shopping site and reviews from a user, information related to a message in an SNS (Social Networking Service) site posted by the user, and the like.

  Moreover, the web server 30 is information on each website, and may acquire behavior related to the behavior of the store manager. For example, the web server 30 may acquire the comment of the store manager who responded to the user review, the comment of the store manager who responded to the user who wants to receive the product, and the like.

  The delivery company device 50 is a server device used by the delivery company. The delivery company apparatus 50 receives a request for shipping products from a store that opens in a shopping mall. In addition, the delivery company device 50 provides the generation device 100 with information related to the distribution of goods.

  As described above, the generation device 100 is a server device that acquires information related to the logistics of a product that is the subject of a commercial transaction, and generates a model that calculates the credibility of the person targeted for the commercial transaction based on the acquired information related to the physical distribution. . The generation device 100 may also serve as the configuration as the web server 30 described above. That is, the generation device 100 itself may provide a shopping mall, and the generation device 100 itself may acquire information from the user terminal 10 that has accessed the shopping mall.

  Note that the generation apparatus 100 may receive information relating to the distribution of the product that is the object of the commercial transaction from the web server 30 or the delivery company apparatus 50. For example, the generation apparatus 100 may crawl the network to obtain information. You may acquire using the predetermined | prescribed program etc. to acquire.

[3. Configuration of the generator
Next, the configuration of the generation apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram illustrating a configuration example of the generation apparatus 100 according to the embodiment. As illustrated in FIG. 3, the generation device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The generation device 100 includes an input unit (for example, a keyboard and a mouse) that receives various operations from an administrator who uses the generation device 100, and a display unit (for example, a liquid crystal display) for displaying various types of information. You may have.

(About the communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card). The communication unit 110 is connected to a communication network by wire or wireless, and transmits and receives information to and from the user terminal 10 and the like via the communication network.

(About the storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 according to the embodiment includes a store information storage unit 121, a user information storage unit 122, a store model storage unit 123, and a user model storage unit 124. Hereinafter, each storage unit will be described in order.

(About the store information storage unit 121)
The store information storage unit 121 stores information related to a store that opens at a predetermined commercial transaction site. For example, the generation apparatus 100 acquires information stored in the store information storage unit 121 from the web server 30 or the delivery company apparatus 50 via the network N. Here, FIG. 4 shows an example of the store information storage unit 121 according to the embodiment. As illustrated in FIG. 4, the store information storage unit 121 includes items such as “store ID” and “store feature information”. In addition, the items of “store feature information” include small items such as “return rate”, “order cancellation rate”, “out of stock product rate”, “out of stock average period”, “store evaluation value”, and “user review index value”. Contains items.

  “Store ID” indicates identification information for identifying a store that has opened a store at a predetermined commercial transaction site. In this specification, the identification information as shown in FIG. 4 is common to the reference numerals used in the description. For example, the store identified by the store ID “A01” indicates “store A01”.

  The “store feature information” indicates information that can be a feature in model generation processing (modeling) by the generation device 100.

  The “return rate” indicates a rate at which a product sent to the user is returned from the user within a predetermined period (for example, one month) due to a store side trouble or a product defect. The “order cancellation rate” indicates the rate at which the store side canceled the product ordered by the user for some reason. Cancellation of an order occurs, for example, when it is determined that the product is out of stock after the order is placed or the ordered product cannot be procured.

  “Out-of-stock product rate” indicates the rate of products out of stock among products handled by the store. The “out-of-stock average period” indicates the average period during which any product is out of stock in the store. For example, the average out-of-stock period indicates an average of a period until the product is out of stock and the out-of-stock state is resolved in a predetermined statistical period (for example, one year). For example, in the case of a store that deals with out of stock relatively quickly, such a period is calculated to be relatively short.

  “Store evaluation value” indicates an evaluation value of a store on a platform such as a shopping mall. The store evaluation value is indicated by, for example, an average value of numerical values of five-step evaluation selected by the user. The store evaluation value may be an evaluation given by a shopping mall manager or the like.

  The “user review index value” indicates an index value that can be an evaluation of a review posted by a user in the store. In the embodiment, the user review index value is indicated by a numerical value in 100 steps. The user review index value is, for example, a phrase that is negatively evaluated by the user among all text data of the review (for example, “delivery is late”, “out of stock”, “packing is bad”, etc.) Is calculated by the web server 30, the generation device 100, or the like based on the amount included in the data. For calculating the user review index value, various known techniques may be used. For example, the generation apparatus 100 labels a review posted to a store having a relatively high store evaluation value as correct data (for example, a store in which the user review index value is determined to be “100”), and the review. A model for calculating a user review index value may be generated by learning a word or the like included in.

  That is, in FIG. 4, as an example of data stored in the store information storage unit 121, the store feature information of the store A01 having the store ID “A01” has a return rate of “3%” and an order cancellation rate is “4%”, out-of-stock product rate is “6%”, average out-of-stock period is “16 days”, store evaluation value is “3.5”, and user review index value is “75”. ".

  Although illustration in FIG. 4 is omitted, the store information storage unit 121 may store various information related to the store in addition to the information described above. For example, the store information storage unit 121 may store information such as store sales, capital and other financial conditions, store continuation years, and the like. Further, the store information storage unit 121 stores the number of users who continue to use the store (for example, users who perform an action such as accessing again within one week) and the user unit price (for example, in a predetermined period). Information such as the purchase amount per user), the number of times the store, the store name, and the product were searched in a predetermined period, and the increase rate of the number of searches. The store information storage unit 121 may store a page view of the store, the number of unique users accessing the store, and the like. Further, the generation apparatus 100 may generate a model for calculating the credit rating of the store using these pieces of information as features.

(User information storage unit 122)
The user information storage unit 122 stores information related to a user who is a purchaser of a product in a commercial transaction. Here, FIG. 5 illustrates an example of the user information storage unit 122 according to the embodiment. As illustrated in FIG. 5, the user information storage unit 122 includes items such as “user ID” and “user feature information”. The items of “user identity information” are small items such as “return rate”, “halfway cancellation rate”, “review suitability”, “delivery success rate”, “delivery date change rate”, and “delivery company review index value”. Contains items.

  The “returned goods rate” indicates the ratio of the number of products returned to the store within a predetermined period (for example, one month) due to the convenience of the user among the products shipped from the store. “Intermediate cancellation rate” indicates the ratio of the number of products canceled out for the convenience of the user before the products are shipped out of the products ordered in the shopping mall.

  “Review suitability” indicates an index value of the content posted by a user on a platform such as a shopping mall. In the embodiment, it is assumed that the review suitability is indicated by a numerical value of 100 levels. The review aptitude is, for example, the percentage of all text data of reviews submitted by users that contains words that abuse the store, or an unnaturally low rating for stores that do not purchase products. It is assumed that it is calculated by the generation device 100 or the web server 30 based on the behavior or the like. For the review suitability, various known techniques may be used in the same manner as the user review index value shown in FIG.

  The “delivery success rate” indicates a rate at which delivery of goods to the user by the delivery company (receipt of goods by the user) was performed according to the designated date and time. For example, the delivery success rate may be the success rate of the first delivery, or the delivery success rate by the first or re-delivery (up to the second). For example, a user with a high delivery success rate is determined to be a trusted user for a shopping mall (or delivery company).

  “Delivery date change rate” indicates the rate at which the delivery date is changed from the user to the delivery company. For example, a user with a very high delivery date change rate is determined to be an untrustworthy user for a shopping mall (or delivery company).

  The “delivery company review index value” indicates an evaluation by the delivery company for the user as an index value. In the embodiment, it is assumed that the delivery company review index value is indicated by a numerical value of 100 levels. For example, the delivery company review index value may be calculated by a score or the like transmitted from a delivery company that has actually delivered to the user. Alternatively, the delivery company review index value is the number of times that a problem behavior such as “received a complaint from the user” or “specified delivery date but not at home” from the delivery company that actually delivered to the user is reported. Or may be calculated based on a ratio or the like.

  That is, in FIG. 5, as an example of data stored in the user information storage unit 122, the user identity information of the user U01 whose user ID is “U01” has a return rate of “5%” and a midway cancellation rate. “13%”, review suitability is “55”, delivery success rate is “60%”, delivery date change rate is “45%”, and delivery company review index value is “40”. It shows that there is.

  In addition, although illustration in FIG. 5 is omitted, the user information storage unit 122 may store various information about the user in addition to the information described above. For example, in the user information storage unit 122, attribute information such as the user's age, sex, and residence, the total purchase price in the store, the average purchase price in the store over a predetermined period, and products purchased in the past A list or the like may be stored. Further, the generation apparatus 100 may generate a model for calculating the user's reliability using these pieces of information as features.

(About the store model storage unit 123)
The store model storage unit 123 stores information related to a model for calculating store credit. Here, FIG. 6 shows an example of the store model storage unit 123 according to the embodiment. As illustrated in FIG. 6, the store model storage unit 123 includes items such as “store model ID” and “feature”. The “feature” item includes a small item corresponding to the store feature information shown in FIG. 4 and a small item indicating the correspondence between the result of each feature and the score.

  “Store model ID” indicates identification information for identifying a store model. “Feature” indicates information about the feature for calculating the creditworthiness of the store.

  In the embodiment, as an example of a model generated by the generation device 100, when a result of store feature information is input, a score corresponding to the result of each input feature is calculated, and the sum of the calculated scores is stored. Take a model that outputs the credit rating of.

  For example, if store information with a return rate of “3%” is input to the store model MS01 shown in FIG. 6, a score “7” corresponding to “˜3” as a result of the return rate is calculated. The Similarly, the score corresponding to each feature is calculated, and the credit rating (score) of the store is calculated based on the sum of the calculated scores. For example, in the store model MS01 shown in the embodiment, it is assumed that the higher the score is, the higher the store reliability is calculated.

  Note that the correspondence between the result and the score illustrated in FIG. 6 is an example, and the generation apparatus 100 may derive the correspondence between the result and the score using various known techniques. For example, the generation apparatus 100 may acquire the correspondence between the result of the feature and the score as illustrated in FIG. 6 by acquiring store information as a large number of samples and statistically analyzing the acquired information. Good. Specifically, the generation apparatus 100 may generate the model so that the average value of the acquired results of each feature is an average in the score (for example, a result of which the score is calculated as “5”). Moreover, although mentioned later for details, the production | generation apparatus 100 may produce | generate a model not only using a statistical method but using a predetermined learning process. In other words, the generation device 100 generates any model as long as it is a model that can calculate the creditworthiness of a person (for example, a store) of a commercial transaction based on the information related to the distribution of the commodity that is the target of the commercial transaction. Also good.

  That is, in FIG. 6, the store model MS01 identified by the store model ID “MS01” includes “return rate”, “order cancellation rate”, “out-of-stock item rate”, “out-of-stock average period”, “store evaluation value”. ”And“ user review index value ”, the score is calculated based on the result information of the respective features, and the credit rating of the store is output based on the calculated score.

(About the user model storage unit 124)
The user model storage unit 124 stores information related to a model for calculating the user's trustworthiness. Here, FIG. 7 illustrates an example of the user model storage unit 124 according to the embodiment. As illustrated in FIG. 7, the user model storage unit 124 includes items such as “user model ID” and “feature”. The “feature” item includes a small item corresponding to the user feature information shown in FIG. 5 and a small item indicating the correspondence between the result of each feature and the score.

  “User model ID” indicates identification information for identifying a model for a user. “Feature” indicates information related to a feature for calculating a user's credit rating.

  In the embodiment, as an example of a model generated by the generation device 100, when a result of user feature information is input, a score corresponding to the input result of each feature is calculated, and the user is calculated from the sum of the calculated scores. Take a model that outputs the credit rating of.

  For example, when information on a user whose return rate is “5%” is input to the user model MU01 shown in FIG. 7, a score “5” corresponding to “˜5” as a result of the return rate is calculated. The Similarly, the score corresponding to each feature is calculated, and the credit level (score) of the user is calculated based on the value obtained by adding the calculated scores. For example, in the user model MU01 shown in the embodiment, it is assumed that the higher the score, the higher the store trustworthiness.

  Note that the correspondence between the result and the score shown in FIG. 7 is an example, and the generation apparatus 100 uses various known techniques in the same way as the store model shown in FIG. May be derived. In other words, the generation device 100 generates any model as long as it is a model that can calculate the creditworthiness of a target person (for example, a user) of a commercial transaction based on information related to the logistics of the commodity that is the target of the commercial transaction. Also good.

  That is, in FIG. 7, the user model MU01 identified by the user model ID “MU01” is “return rate”, “halfway cancellation rate”, “review suitability”, “delivery success rate”, “delivery date change rate”. , The score is calculated based on the result information of each feature of the “deliverer review index value”, and the credit rating of the user is output based on the calculated score.

(About the control unit 130)
The control unit 130 is a controller, for example, various programs (an example of a generation program) stored in a storage device inside the generation apparatus 100 by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. This is realized by executing the RAM as a work area. The control unit 130 is a controller, and is realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  As illustrated in FIG. 3, the control unit 130 according to the embodiment includes an acquisition unit 131, a generation unit 132, and a calculation unit 133, and implements or executes information processing functions and operations described below. Note that the internal configuration of the control unit 130 is not limited to the configuration illustrated in FIG. 3, and may be another configuration as long as the information processing described below is performed. Further, the connection relationship between the processing units included in the control unit 130 is not limited to the connection relationship illustrated in FIG. 3, and may be another connection relationship.

(About the acquisition unit 131)
The acquisition unit 131 acquires various types of information. For example, the acquisition unit 131 acquires information regarding the physical distribution of the product that is the object of the commercial transaction.

  Specifically, the acquisition unit 131 acquires, as information relating to logistics, a return rate at which merchandise is returned after the store ships the merchandise. For example, the acquisition unit 131 may acquire the return rate calculated based on the transaction status in the shopping mall from the web server 30. Alternatively, the acquisition unit 131 may acquire information on the shipment and return of the product from the delivery company device 50, and may acquire the return rate of the product based on the acquired information. For example, the acquisition unit 131 acquires the return rate of products for each store that opens in a shopping mall.

  Further, the acquisition unit 131 may acquire shipping information indicating whether or not the shipping of the product has been completed when the store receives an order for a commercial transaction, as information relating to logistics. Note that the shipment of the product can be completed means, for example, that the product has not been out of stock and that the order from the user can be responded without any problem. For example, the acquisition unit 131 may acquire the shipping information from the web server 30 or may acquire from the delivery company device 50 that the delivery of the product to the user has been completed. Note that the acquisition unit 131 may calculate information such as the order cancellation rate and the out-of-stock rate shown in FIG. 4 based on the shipping information, and may acquire the calculated numerical value. In addition, the acquisition unit 131 may acquire the store out-of-stock product rate, the out-of-stock average period, and the store evaluation value from the web server 30 as appropriate as information relating to logistics.

  Moreover, the acquisition part 131 may acquire the user review regarding dispatch of the goods by a store as information regarding physical distribution. For example, the acquisition unit 131 acquires a user review posted to a shopping mall from an arbitrary store from the web server 30. Further, the acquisition unit 131 may calculate the user review index value and the like illustrated in FIG. 4 based on the user review regarding the shipment of the product. In this case, the acquisition unit 131 performs, for example, a morphological analysis on a user review when tabulating information on text such as a user review, and further appears in a review such as tf-idf (Term Frequency-Inverse Document Frequency). Alternatively, an algorithm for scoring each word to be used may be used to obtain only words having a high weight. Then, the acquisition unit 131 may digitize the user review based on the ratio of negative words such as “slow”, “bad”, and “miscellaneous” in the reviews. As described above, various known techniques may be used for such processing. That is, the acquisition unit 131 may use any method as long as information (for example, a numerical value) that can be used to evaluate a store based on a user review can be acquired.

  Moreover, the acquisition part 131 may further acquire the information regarding the evaluation from the user with respect to the store in a predetermined | prescribed commercial transaction site as information regarding a store with the information regarding physical distribution. For example, the acquisition unit 131 may acquire numerical values such as the number of reactions from the user to the store and page views for the store and products.

  In addition, the acquisition unit 131 may acquire information regarding the transition of the number of reactions from the user to the store or the product, or the transition of the evaluation value scored by the user for the store or the product. That is, the acquisition unit 131 may acquire an increase / decrease in the number of reviews submitted in a predetermined period or a change in the score increase / decrease. Thereby, the acquisition part 131 can acquire the information regarding the tendency that the store which received low evaluation from the user once received high evaluation in recent years.

  In addition, when the store financial information can be acquired from, for example, a financial institution or a financial management company, the acquisition unit 131 may acquire such information. When the acquisition unit 131 acquires financial information, the generation unit 132, which will be described later, may generate a model that calculates the credit rating in consideration of the financial information.

  Moreover, the acquisition part 131 may acquire the information regarding the physical distribution in a user. For example, the acquisition unit 131 corresponds to the return application rate (for example, “return rate” shown in FIG. 5) in which, after the user purchases a product, an application for returning the product is performed as information relating to logistics. ) To get. For example, the acquisition unit 131 may acquire information from the web server 30 that stores transactions between the user and the store, or acquire information from the delivery company device 50 that is requested to return to the store from the user. May be.

  Moreover, the acquisition part 131 may acquire the information which shows having canceled before dispatch and receipt of goods after the user ordered goods as information regarding physical distribution. The acquisition unit 131 may calculate the midway cancellation rate shown in FIG. 5 based on such information and acquire the calculated numerical value.

  Moreover, the acquisition part 131 may acquire the user review regarding delivery of goods as information regarding physical distribution. Moreover, the acquisition part 131 may calculate the review suitability shown in FIG. 5 based on the acquired user review, and may acquire the calculated numerical value.

  In addition, the acquisition unit 131 may acquire product reception information indicating whether the user has received the product at the specified delivery date of the product, as information relating to logistics. For example, the acquisition unit 131 acquires product receipt information from the delivery company device 50. And the acquisition part 131 calculates the delivery success rate, delivery date change rate, etc. which are shown in FIG. 5 based on the acquired information, and acquires the calculated numerical value.

  Further, the acquisition unit 131 may acquire evaluation information indicating a result of evaluating the user, which is information transmitted from a delivery company that has delivered the product to the user as information relating to physical distribution. For example, the acquisition unit 131 acquires evaluation information from the delivery company device 50. And the acquisition part 131 calculates the delivery company review index value etc. which are shown in FIG. 5 based on the acquired information, and acquires the calculated numerical value.

  The acquisition unit 131 stores the acquired information in the storage unit 120 as appropriate. For example, the acquisition unit 131 stores information about the acquired store in the store information storage unit 121. The acquisition unit 131 stores information about the acquired user in the user information storage unit 122.

  Note that the acquisition unit 131 may randomly acquire various store and user information without specifying the store or user from which the information is acquired. For example, the acquisition unit 131 uses a program such as a search robot used for a search engine or the like to crawl on the Internet to acquire information on stores and users as needed, or update the acquired information. May be.

(About the generator 132)
The generation unit 132 generates a model for calculating the creditworthiness of the target person of the commercial transaction based on the information regarding the physical distribution acquired by the acquisition unit 131.

  For example, the generation unit 132 generates a model for calculating the credit rating of a product provider (a store in the embodiment) in a commercial transaction as a target of the commercial transaction.

  Specifically, the generation unit 132 generates a model for calculating the credit rating of the store based on the return rate acquired by the acquisition unit 131. For example, the generation unit 132 generates a model such that the higher the return rate in the store, the lower the credit level is calculated, and the lower the return rate, the higher the credit level is calculated.

  Further, the generation unit 132 generates a model for calculating the credit rating of the store based on the shipping information acquired by the acquisition unit 131. Specifically, the generation unit 132 generates a model based on an order cancellation rate calculated based on the shipping information, an out-of-stock product rate, and the like. For example, the generation unit 132 generates a model such that the higher the order cancellation rate or the out-of-stock product rate is, the lower the reliability is calculated, and the lower the order cancellation rate or the out-of-stock product rate is, the higher the reliability is calculated. Note that the generation unit 132 may generate a model such that the shorter the out-of-stock average period is, the higher the reliability is calculated, and the longer the out-of-stock average period is, the lower the reliability is calculated.

  In addition, the generation unit 132 generates a model for calculating the credit rating of the store based on a user review regarding shipping of products by the store. Specifically, the generation unit 132 generates a model based on a user review index value calculated based on a user review, a store evaluation value acquired together with the user review, and the like. For example, the generation unit 132 generates a model such that the higher the store evaluation value or the user review index value, the higher the reliability is calculated, and the lower the store evaluation value or the user review index value, the lower the reliability is calculated.

  Moreover, the production | generation part 132 produces | generates the model which calculates the creditworthiness of the purchaser of goods (in this embodiment, the user in a shopping mall) as a commercial transaction target person.

  For example, the generation unit 132 generates a model for calculating the user's credit rating based on the return application rate performed by the user. Specifically, the generation unit 132 generates a model in which the higher the return application rate (corresponding to the return rate shown in FIG. 5) is, the lower the credit is calculated, and the higher the return application rate is, the lower the credit is calculated. . The generation unit 132 may generate a model such that the higher the midway cancellation rate is, the lower the reliability is calculated, and the lower the midway cancellation rate is, the higher the reliability is calculated.

  Moreover, the production | generation part 132 produces | generates the model which calculates a user's reliability based on the user review regarding dispatch of the goods by a store. Specifically, the generation unit 132 generates a model based on the review suitability calculated based on the user review. For example, the generation unit 132 generates a model such that the higher the review suitability is, the higher the trustworthiness is calculated, and the lower the review suitability is, the lower the trustworthiness is calculated.

  Further, the generation unit 132 generates a model for calculating the user's credit rating based on the product reception information indicating whether or not the user has received the product at the delivery specified date and time of the product. Specifically, the generation unit 132 generates a model based on a delivery success rate, a delivery date change rate, and the like calculated based on the product receipt information. For example, the generation unit 132 is calculated such that the higher the delivery success rate is or the lower the delivery date change rate is, the higher the reliability is, and the lower the delivery success rate is or the higher the delivery date change rate is, the lower the reliability is calculated. A simple model.

  In addition, the generation unit 132 is a model that calculates the user's reliability based on evaluation information that is transmitted from the delivery company that delivered the product to the user and indicates a result of the delivery company evaluating the user. Generate. Specifically, the generation unit 132 generates a model based on the delivery company review index value calculated based on the evaluation information. For example, the generation unit 132 generates a model such that the higher the delivery company review index value is, the higher the reliability is calculated, and the lower the delivery company review index value is, the lower the reliability is calculated.

  For example, the generation unit 132 inputs information on each feature of the target store or user as shown in FIGS. 6 and 7, calculates a score of each feature corresponding to the input result, and calculates the calculated score. Generate a model that adds up and outputs the total score.

  The model output by the generation unit 132 is not limited to the above example, and any model can be used as long as it can calculate (numerize) creditworthiness based on information relating to physical distribution as exemplified above. It may be.

  For example, the generation unit 132 obtains result information of each feature of a store that is a positive example (for example, the top 1000 stores having a high evaluation value in a shopping mall) and a negative example (for example, a low order 1000 having a low evaluation value in a shopping mall). The result information of each feature of the store that is the store) may be learned by a regression equation to generate a model. For example, the generation unit 132 creates the following formula (1).

y = ω ₁ · x ₁ + ω ₂ · x ₂ + ω ₃ · x ₃ ... + ω _N · x _N (1) (N is an arbitrary number)

  The above formula (1) is created for each individual store, for example. In the above formula (1), “y” indicates whether the store is a positive example or a negative example. For example, “1” is input to “y” for a positive example, and “0” is input to “y” for a negative example.

In the above equation (1), “x” is the store feature information and corresponds to the explanatory variable in the regression equation. In the above formula (1), “ω” is a coefficient of “x” and indicates a predetermined weight value. Specifically, “ω ₁ ” is a weight value of “x ₁ ”, “ω ₂ ” is a weight value of “x ₂ ”, and “ω ₃ ” is a weight value of “x ₃ ”. It is. As described above, the above equation (1) is a variable including the explanatory variable “x” corresponding to the feature information extracted from the information related to physical distribution and the predetermined weight value “ω” (for example, “ω ₁ · x ₁ It is created by combining the _"). For example, in the above formula (1), “x ₁ ” is the “return rate” shown in FIG. 4, and “x ₂ ” is the “order cancellation rate” shown in FIG.

The generation unit 132 generates the expression (1) for each store. Then, the generation unit 132 sets the generated expression as a machine learning sample. Then, the generation unit 132 derives a value corresponding to the predetermined weight value “ω” by performing calculation processing (regression learning) of a sample expression. That is, the generation unit 132 determines a predetermined weight value “ω” that satisfies the above formula (1). In other words, the generation unit 132 determines the weight value “ω” indicating the influence of the predetermined explanatory variable on the objective variable “y”. For example, if the “return rate” contributes significantly to the event of “credit (in this example, an evaluation value in a shopping mall)” compared to other variables, it corresponds to the “return rate”. The value of the weight value “ω ₁ ” is calculated to be larger than other variables.

  In the above example, the return rate, the order cancellation rate, etc. are shown as features. However, when the return rate or the order cancellation rate is substituted into equation (1), it can be used as an explanatory variable by a predetermined process. May be converted to a numeric value. For example, the generation unit 132 normalizes and uses these numerical values by a known method. Further, in the generation process using the above equation (1), instead of setting the left side to “1” or “0”, a predetermined error is assumed, and a value obtained by squaring the difference from the error is the minimum value. The optimal solution of “ω” may be calculated using a method such as a least square method that approximates the above. Further, the generation unit 132 may perform various design changes such as a store having a high evaluation value in the shopping mall as a positive example, and a store that meets other predetermined conditions as a positive example. Further, the generation unit 132 may generate a model using the same method for not only the store but also the user.

  In addition, although description was abbreviate | omitted in said example, the production | generation part 132 may produce | generate a model using all the features regarding a store or a user. Moreover, the model generation method illustrated is an example, and the model generated by the generation unit 132 is not limited to the above example. That is, the generation unit 132 is a model that is generated based on information related to physical distribution, and is a known model that outputs the creditworthiness of a target person of a commercial transaction when information about the target person of the commercial transaction is input. A model different from the above example may be appropriately generated by combining the methods. For example, in the above example, an example in which a model is generated by regression analysis has been shown. However, the generation unit 132 may generate a model by other statistical processing, or a model using a neural network or the like. It may be generated. In the learning process (machine learning), various methods such as linear regression, logistic regression, support vector machine, decision tree, and hazard analysis may be used.

  The generation unit 132 stores the generated store model in the store model storage unit 123. The generation unit 132 stores the generated user model in the user model storage unit 124.

(About the calculation unit 133)
The calculation unit 133 calculates the creditworthiness of the target person for the commercial transaction. Specifically, the calculation unit 133 inputs information on a target person of a certain commercial transaction to the model generated by the generation unit 132, and calculates the credit rating of the target person of the commercial transaction.

  For example, the calculation unit 133 causes the store model generated by the generation unit 132 to input information relating to physical distribution in the store, thereby causing the store score to be output. The output score indicates the credit rating of the store. Moreover, the calculation part 133 outputs the said user's score by inputting the information regarding the physical distribution in a user into the model for users which the production | generation part 132 produced | generated. The output score indicates the trustworthiness of the user.

  The calculation unit 133 calculates the credit rating of the store or the user based on the output score. The calculation unit 133 may use the output score itself as the credit rating of the store or the user, or may perform processing such as normalizing the output score, and may use the value calculated after the processing as the credit rating of the store or the user. . For example, the calculation unit 133 may perform a process of normalizing the score output from the model to a numerical value of 100 levels, and a value from 1 to 100 calculated after the process may be used as the credit rating of the store or the user.

  For example, the calculation unit 133 may receive a request from the web server 30 and calculate the reliability of users and stores, or may calculate the reliability of all users and stores related to a certain shopping mall. .

[4. Processing procedure)
Next, a generation process procedure performed by the generation apparatus 100 according to the embodiment will be described with reference to FIGS. 8 and 9. FIG. 8 is a flowchart (1) illustrating a processing procedure performed by the generation apparatus 100 according to the embodiment.

  As illustrated in FIG. 8, the generation apparatus 100 acquires information about a store and a user from, for example, the web server 30 (Step S101). Further, the generation apparatus 100 acquires information related to physical distribution in the commercial transaction from the delivery company (step S102).

  And the production | generation apparatus 100 statistics the acquired information (step S103). The generation apparatus 100 generates each model for calculating the credit rating of the user and the store based on the result of the statistical processing (Step S104).

  Subsequently, the generating apparatus 100 stores each generated model in the storage unit 120 (step S105).

  Next, the procedure of the credit calculation process performed by the generation apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 9 is a flowchart (2) illustrating a processing procedure performed by the generation apparatus 100 according to the embodiment.

  As illustrated in FIG. 9, the generation apparatus 100 determines whether or not the target person information has been acquired (step S <b> 201). If the information has not been acquired, the generation device 100 waits until it is acquired (step S201; No).

  On the other hand, when the information of the target person is acquired (step S201; Yes), the generation apparatus 100 inputs the acquired information to the store or the user model (step S202).

  And the production | generation apparatus 100 outputs a score from a model (step S203). Furthermore, the generation device 100 calculates the credit rating of the store or the user based on the output score (Step S204).

[5. (Modification)
The generation apparatus 100 according to the above-described embodiment may be implemented in various different forms other than the above-described embodiment. Therefore, in the following, another embodiment of the generation device 100 will be described.

[5-1. Data to be input to model)
In the above-described embodiment, the generation apparatus 100 has shown an example in which information related to a store or a user is input to the generated model and the credit rating is calculated. Here, the generation apparatus 100 may associate the acquisition time of the information used for generating the model with the store or user information input to the model.

  Since the information acquired by the generation apparatus 100 is information on a commercial transaction site such as a shopping mall, there is a possibility that the information is influenced by the time. For example, there may be a difference in the number of shipments and the inventory status of each store between the time when transactions are actively performed such as the end of the year and other times. For this reason, the generation device 100 stores the time when information is acquired to generate a model, and when the store or user information to be processed is input, the information used for generating the model is acquired. Enter the information obtained at a time similar to the date. Thereby, the production | generation apparatus 100 can perform the production | generation process which considered the time influence of the score output.

[5-2. Amount of information)
In the above embodiment, the generation apparatus 100 has shown an example in which a model is generated based on various information that can be acquired on a network. Here, the generation apparatus 100 may perform processing using only information acquired from general users who use various websites that exceeds a certain threshold.

  For example, reviews regarding products in a certain store, user evaluations, and the like may show a biased tendency if they are not based on a certain number or more of data. In this case, since the influence of biased data tends to be applied to the regression equation, the generation apparatus 100 may generate a model that cannot accurately calculate the credit rating of the store or the user. For this reason, the generation apparatus 100 may use only data whose score and review total number transmitted from the user to the store exceed a certain number as data handled in the model generation process. Thereby, the generating apparatus 100 can generate a model that outputs a highly reliable score.

[5-3. Information variations)
In the above embodiment, the generation apparatus 100 has shown an example in which a model is generated based on a user review or the like posted to a shopping mall or the like. Here, the variation of information handled by the generation apparatus 100 will be described in more detail.

  For example, the generation apparatus 100 may perform morphological analysis on the acquired word as information included in the user review, and acquire information aggregated for each part of speech. Specifically, the generation apparatus 100 acquires a total result obtained by classifying noun categories into people, things, places, and the like. Further, the generation apparatus 100 may acquire a classification result such as whether each word has a positive attribute or a negative attribute, or a determination result such as determination of the intention of the dialog when the message is in an interactive format.

  Further, the generation device 100 may acquire the audio information transmitted from the user as an example of a user review. That is, the generation apparatus 100 may acquire the text information corresponding to the user review by recognizing the voice transmitted from the user using voice search or voice input.

  Further, the generation apparatus 100 may extract only a specific part of speech regarding the acquired word. For example, the generating apparatus 100 may use a part of speech that can directly represent the store or the user's situation, such as a noun or an adjective, for processing. As a result, the generation apparatus 100 can reduce the processing burden without reducing the accuracy of the model to be generated.

  Further, the generation apparatus 100 may use only pre-registered words for the score calculation process. For example, the generation apparatus 100 may use words that can express the evaluation of physical distribution, such as “good”, “bad”, “(delivery) slow”, “fast”, etc., for processing. Such registration is performed by an administrator of the generation apparatus 100, for example.

  In addition, the generation apparatus 100 may acquire text data such as user reviews not only on the shopping mall platform but also widely on the network. For example, the generating apparatus 100 may acquire text data corresponding to a user review from an SNS, a user's mail, a web page on which information related to a shopping mall is posted, or the like.

[5-4. (Visualization of creditworthiness)
The generation device 100 may perform processing for visualizing the calculated reliability. For example, when the generation apparatus 100 receives a request for calculating the credit rating from the user or the store, the generating apparatus 100 calculates the credit rating of the user or the store in response to the request. And the production | generation apparatus 100 displays the calculated reliability on the screen of the user terminal 10 or the store terminal 20, for example. For example, the generating apparatus 100 may indicate a calculated numerical value of reliability or may be displayed as a predetermined graph. As an example, the generation apparatus 100 may visualize the credit rating of the user or the store using a chart or a pie chart that allows the score of each feature information to be understood at a glance.

  Further, the generation device 100 may perform a predetermined process using the reliability. For example, the generation apparatus 100 (or the web server 30) grants a right to obtain a novelty to a user having a credit rating exceeding a predetermined threshold, or a store having a credit rating exceeding a predetermined threshold has an evaluation value in a shopping mall. You may perform the process of raising.

[6. Others]
In addition, among the processes described in the above embodiment, all or part of the processes described as being automatically performed can be performed manually, or the processes described as being performed manually can be performed. All or a part can be automatically performed by a known method. In addition, the processing procedures, specific names, and information including various data and parameters shown in the document and drawings can be arbitrarily changed unless otherwise specified.

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured.

  For example, the information in the storage unit 120 illustrated in FIG. 3 may be stored in an external storage server or the like without the generation apparatus 100. In this case, the generation apparatus 100 acquires various pieces of stored information by accessing the storage server.

  Further, for example, the generation apparatus 100 described above performs a generation process and the like on the front-end server side that mainly executes exchanges with external apparatuses such as acquiring various types of information and accepting requests for calculation of store trust. You may distribute to the back-end server side to perform. In this case, for example, the front-end server includes at least the acquisition unit 131. Further, the back-end server has at least a generation unit 132.

[7. Hardware configuration)
Further, the generation device 100, the user terminal 10, and the like, the store terminal 20, the web server 30, and the delivery company device 50 according to the above-described embodiments are realized by a computer 1000 having a configuration as shown in FIG. Hereinafter, the generation apparatus 100 will be described as an example. FIG. 10 is a hardware configuration diagram illustrating an example of a computer 1000 that implements the functions of the generation apparatus 100. The computer 1000 includes a CPU 1100, RAM 1200, ROM 1300, HDD 1400, communication interface (I / F) 1500, input / output interface (I / F) 1600, and media interface (I / F) 1700.

  The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400 and controls each unit. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, a program depending on the hardware of the computer 1000, and the like.

  The HDD 1400 stores a program executed by the CPU 1100, data used by the program, and the like. The communication interface 1500 receives data from other devices via the communication network 500 (corresponding to the network N shown in FIG. 2) and sends the data to the CPU 1100, and other data generated by the CPU 1100 via the communication network 500 To the device.

  The CPU 1100 controls an output device such as a display and a printer and an input device such as a keyboard and a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. Further, the CPU 1100 outputs the data generated via the input / output interface 1600 to the output device.

  The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads such a program from the recording medium 1800 onto the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. Etc.

  For example, when the computer 1000 functions as the generation apparatus 100, the CPU 1100 of the computer 1000 implements the function of the control unit 130 by executing a program loaded on the RAM 1200. In addition, each data in the storage unit 120 is stored in the HDD 1400. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them, but as another example, these programs may be acquired from other devices via the communication network 500.

[8. effect〕
As described above, the generation device 100 according to the embodiment includes the acquisition unit 131 and the generation unit 132. The acquisition unit 131 acquires information regarding the physical distribution of the product that is the object of the commercial transaction. The generation unit 132 generates a model for calculating the creditworthiness of the target person of the commercial transaction based on the information regarding the physical distribution acquired by the acquisition unit 131.

  As described above, the generation device 100 according to the embodiment generates a model for calculating the credibility of the target person of the commercial transaction from the viewpoint of product distribution. Thereby, since the production | generation apparatus 100 can produce | generate a model based on the information regarding dispatch of actual goods, it can produce | generate the model which can calculate the trustworthiness according to the actual condition. As a result, the generation apparatus 100 can determine the credibility of the target person of the commercial transaction from various viewpoints.

  Moreover, the production | generation part 132 produces | generates the model which calculates the creditworthiness of the provider (product store in embodiment) of the goods in a commercial transaction as an object person of a commercial transaction.

  As described above, the generating apparatus 100 according to the embodiment determines the trustworthiness of the provider from various viewpoints by generating a model for calculating the trustworthiness of the provider of the product in the commercial transaction based on the information on the physical distribution. be able to.

  Moreover, the acquisition part 131 acquires the return rate in which the said goods were returned after the provider shipped goods as information regarding physical distribution. The generation unit 132 generates a model for calculating the trustworthiness of the provider based on the return rate.

  As described above, the generation device 100 according to the embodiment generates a model based on the return rate, so that, for example, the reliability of a provider who frequently returns merchandise or a provider who easily generates useless delivery of merchandise is increased. It is possible to determine the trustworthiness of the provider from various viewpoints such as calculating low.

  In addition, the acquisition unit 131 acquires shipping information indicating whether or not the shipping of the product has been completed when the provider receives an order for a commercial transaction, as information relating to logistics. The generation unit 132 generates a model for calculating the trustworthiness of the provider based on the shipping information.

  As described above, the generation apparatus 100 according to the embodiment provides a model from various viewpoints by generating a model based on the shipping information, for example, calculating a low reliability for a provider that is likely to be canceled due to lack of inventory. The creditworthiness of the person can be determined.

  Moreover, the acquisition part 131 acquires the user review regarding dispatch of the goods by a provider as information regarding physical distribution. The production | generation part 132 produces | generates the model which calculates a provider's creditworthiness based on the user review regarding dispatch of the goods by a provider.

  As described above, the generation apparatus 100 according to the embodiment generates a model based on a user review regarding shipping, for example, for a provider with a bad reputation from the user or a provider that easily causes a delay in a product. The creditworthiness of the provider can be determined from various viewpoints, such as calculating a low value.

  Moreover, the production | generation part 132 produces | generates the model which calculates the creditworthiness of the purchaser (product in embodiment) of the goods in commercial transaction as a target person of commercial transaction.

  As described above, the generating apparatus 100 according to the embodiment determines the creditworthiness of the purchaser from various viewpoints by generating a model for calculating the creditworthiness of the purchaser of the product in the commercial transaction based on the information related to physical distribution. be able to.

  Moreover, the acquisition part 131 acquires the return application rate which applied for returning the said goods after a purchaser purchased goods as information regarding distribution. The generation unit 132 generates a model for calculating the creditworthiness of the purchaser based on the return application rate.

  As described above, the generation device 100 according to the embodiment generates a model based on the return application rate, for example, about a purchaser who frequently requests returns or a purchaser who easily increases the delivery amount of goods. Can determine the creditworthiness of the purchaser from various viewpoints, such as calculating a low creditworthiness.

  Moreover, the acquisition part 131 acquires the user review regarding delivery of goods as information regarding physical distribution. The production | generation part 132 produces | generates the model which calculates a buyer's creditworthiness based on the user review regarding dispatch of the goods by a purchaser.

  As described above, the generation apparatus 100 according to the embodiment generates a model based on the suitability degree of the user review described by the user, for example, for a purchaser who takes an action such as frequently describing unreasonable claims. Can determine the creditworthiness of the purchaser from various viewpoints, such as calculating a low creditworthiness.

  In addition, the acquisition unit 131 acquires product receipt information indicating whether the purchaser has received the product at the delivery date and time specified for the product as information relating to logistics. The generation unit 132 generates a model for calculating the creditworthiness of the purchaser based on the product receipt information.

  As described above, the generation apparatus 100 according to the embodiment generates a model based on the degree of completion of product delivery (delivery success rate), the delivery date change rate, and the like, for example, the frequency of simply purchasing the product. It is possible to judge the creditworthiness of buyers from various viewpoints, such as calculating high creditworthiness for buyers who are not high buyers but who are interested in smooth logistics and buyers who do not cause unnecessary redelivery. .

  In addition, the acquisition unit 131 acquires evaluation information indicating the result of evaluating the purchaser, which is information transmitted from the delivery company that delivered the product to the purchaser as information relating to logistics. The generation unit 132 generates a model for calculating the purchaser's credit rating based on the evaluation information.

  As described above, the generation apparatus 100 according to the embodiment generates a model based on the reputation of the trader who actually delivered the product, for example, a purchaser who easily attaches an unreasonable claim to the delivery trader, The creditworthiness of the purchaser can be determined from various viewpoints, such as calculating a low creditworthiness for a purchaser who is likely to be shy.

  The embodiments of the present application and some of the modifications of the embodiments have been described in detail with reference to the drawings. However, these are examples, and the knowledge of those skilled in the art including the aspects described in the disclosure section of the invention is included. The present invention can be implemented in other forms based on various modifications and improvements.

  Further, the generation apparatus 100 described above may be realized by a plurality of server computers, and depending on functions, an external platform or the like may be realized by calling an API (Application Programming Interface), network computing, or the like. Can be changed flexibly.

  Further, “section (module, unit)” described in the claims can be read as “means”, “circuit”, and the like. For example, the generation unit can be read as generation means or a generation circuit.

DESCRIPTION OF SYMBOLS 1 Generation system 10 User terminal 20 Store terminal 30 Web server 50 Delivery company apparatus 100 Generation apparatus 110 Communication part 120 Storage part 121 Store information storage part 122 User information storage part 123 Model storage part for store 124 Model storage part for user 130 Control part 131 Acquisition Unit 132 Generation Unit 133 Calculation Unit

Claims (14)

An acquisition unit that acquires information on the logistics of products subject to commerce;
Based on information related to logistics acquired by the acquisition unit, a generation unit that generates a model for calculating a credit rating of a purchaser of a product in the commercial transaction among the target persons of the commercial transaction ;
Equipped with a,
The acquisition unit
As information on the physical distribution, obtain a user review on the delivery of the product,
The generator is
Based on a user review regarding shipping of the product by the purchaser, generate a model for calculating the creditworthiness of the purchaser.
A generating device characterized by that. An acquisition unit that acquires information on the logistics of products subject to commerce;
Based on information related to logistics acquired by the acquisition unit, a generation unit that generates a model for calculating a credit rating of a purchaser of a product in the commercial transaction among the target persons of the commercial transaction;
With
The acquisition unit
As the information regarding the physical distribution, obtaining product receipt information indicating whether or not the purchaser has received the product at the delivery specified date and time of the product,
The generator is
Generating a model for calculating the creditworthiness of the purchaser based on the product receipt information;
A generating device characterized by that. An acquisition unit that acquires information on the logistics of products subject to commerce;
Based on information related to logistics acquired by the acquisition unit, a generation unit that generates a model for calculating a credit rating of a purchaser of a product in the commercial transaction among the target persons of the commercial transaction;
With
The acquisition unit
Information relating to the physical distribution is information transmitted from a delivery company that delivered the product to the purchaser, and obtains evaluation information indicating a result of evaluating the purchaser,
The generator is
Based on the evaluation information, a model for calculating the creditworthiness of the purchaser is generated.
A generating device characterized by that. The generator is
Further, as a subject of the commercial transaction, a model for calculating a credit rating of a product provider in the commercial transaction is generated.
The generating apparatus according to claim 1, wherein The acquisition unit
As the information on the physical distribution, after the provider ships the product, the return rate of the product returned is obtained,
The generator is
Generating a model for calculating the creditworthiness of the provider based on the return rate;
The generating apparatus according to claim 4 . The acquisition unit
As the information on the physical distribution, obtaining shipping information indicating whether or not the shipping of the product has been completed when the provider accepts an order for a commercial transaction,
The generator is
Based on the shipping information, a model for calculating the trustworthiness of the provider is generated.
The generating apparatus according to claim 4 or 5 , wherein The acquisition unit
As information on the physical distribution, obtain a user review on shipping of the product by the provider,
The generator is
Generating a model for calculating the trustworthiness of the provider based on a user review on shipping of the product by the provider;
The generating apparatus according to claim 4 , wherein The acquisition unit
As the information on the physical distribution, after the purchaser purchases the product, a return application rate for applying for a return of the product is obtained,
The generator is
Generating a model for calculating the creditworthiness of the purchaser based on the return application rate;
The generating apparatus according to claim 1, wherein A generation method executed by a computer,
An acquisition process for acquiring information related to the logistics of products subject to commerce;
Based on information related to logistics acquired by the acquisition step, a generation step of generating a model for calculating the creditworthiness of the purchaser of the product in the commercial transaction among the target persons of the commercial transaction ;
Only including,
The acquisition step includes
As information on the physical distribution, obtain a user review on the delivery of the product,
The generating step includes
Based on a user review regarding shipping of the product by the purchaser, generate a model for calculating the creditworthiness of the purchaser.
A generation method characterized by that. An acquisition procedure to obtain information about the logistics of the goods subject to commerce;
A generation procedure for generating a model for calculating the creditworthiness of the purchaser of the product in the commercial transaction among the target persons of the commercial transaction based on the information on the logistics acquired by the acquisition procedure;
To the computer,
The acquisition procedure is as follows:
As information on the physical distribution, obtain a user review on the delivery of the product,
The generation procedure is as follows:
Based on a user review regarding shipping of the product by the purchaser, generate a model for calculating the creditworthiness of the purchaser.
A generation program characterized by that. A generation method executed by a computer,
An acquisition process for acquiring information related to the logistics of products subject to commerce;
Based on information related to logistics acquired by the acquisition step, a generation step of generating a model for calculating a credit rating of a purchaser of the product in the commercial transaction among the target persons of the commercial transaction;
Including
The acquisition step includes
As the information regarding the physical distribution, obtaining product receipt information indicating whether or not the purchaser has received the product at the delivery specified date and time of the product,
The generating step includes
Generating a model for calculating the creditworthiness of the purchaser based on the product receipt information;
A generation method characterized by that. An acquisition procedure to obtain information about the logistics of the goods subject to commerce;
A generation procedure for generating a model for calculating the creditworthiness of the purchaser of the product in the commercial transaction among the target persons of the commercial transaction based on the information on the logistics acquired by the acquisition procedure;
To the computer,
The acquisition procedure is as follows:
As the information regarding the physical distribution, obtaining product receipt information indicating whether or not the purchaser has received the product at the delivery specified date and time of the product,
The generation procedure is as follows:
Generating a model for calculating the creditworthiness of the purchaser based on the product receipt information;
A generation program characterized by that. A generation method executed by a computer,
An acquisition process for acquiring information related to the logistics of products subject to commerce;
Based on information related to logistics acquired by the acquisition step, a generation step of generating a model for calculating a credit rating of a purchaser of the product in the commercial transaction among the target persons of the commercial transaction;
Including
The acquisition step includes
Information relating to the physical distribution is information transmitted from a delivery company that delivered the product to the purchaser, and obtains evaluation information indicating a result of evaluating the purchaser,
The generating step includes
Based on the evaluation information, a model for calculating the creditworthiness of the purchaser is generated.
A generation method characterized by that. An acquisition procedure to obtain information about the logistics of the goods subject to commerce;
A generation procedure for generating a model for calculating the creditworthiness of the purchaser of the product in the commercial transaction among the target persons of the commercial transaction based on the information on the logistics acquired by the acquisition procedure;
To the computer,
The acquisition procedure is as follows:
Information relating to the physical distribution is information transmitted from a delivery company that delivered the product to the purchaser, and obtains evaluation information indicating a result of evaluating the purchaser,
The generation procedure is as follows:
Based on the evaluation information, a model for calculating the creditworthiness of the purchaser is generated.
A generation program characterized by that.

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