Information processing device, information processing method, and information processing program

Abstract

To provide an information processing device, an information processing method, and an information processing program that can propose a product to a user on the basis of ranking reflecting an appropriate transaction completion prediction.SOLUTION: An information processing device according to the present disclosure includes: an acquisition unit that acquires user information indicating information related to a user from a user terminal; a reception unit that accepts order information indicating information related to an order of the user for a product from the user terminal; a prediction unit that predicts, on the basis of the user information, the order information, product information, and store information, a predicted cancellation rate of the ordered product; a calculation unit that calculates a predicted number of transactions for each product in a predetermined period on the basis of the predicted cancellation rate; and a generation unit that generates product ranking on the basis of the predicted number of successful transactions for each product.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present disclosure relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] In some cases, an electronic commerce site may suggest products to users for purchase. In this case, the products to be suggested are ranked based on some criteria, and the products to be suggested are determined based on the ranking.

[0003] For example, Patent Document 1 listed below discloses a product proposal system in which a customer sends a customer image taken with a digital camera to a product proposal server via the Internet, the product proposal server provides the customer image to a product proposer, the customer image is received from the product proposal server by a product proposal terminal device, the customer image is processed to make a proposal for the customer, a proposed image is created and sent to the product proposal server, the product proposal server provides the proposed image to the customer, and the customer receives and views the proposed image on a viewing device. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2002-366837 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, although the product recommendation system described in Patent Document 1 above can suggest products to customers based on image data taken by the customers, it cannot suggest products to customers based on information ranked according to criteria such as exact sales volume.

[0006] In consideration of the above-mentioned problems, the present disclosure aims to provide an information processing device, an information processing method, and an information processing program that can suggest products to users based on rankings that reflect appropriate transaction completion predictions. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems and achieve the objective, the information processing device of the present disclosure includes an acquisition unit that acquires user information indicating information about a user from a user terminal, a reception unit that accepts order information indicating information regarding the user's order for a product from the user terminal, a prediction unit that predicts a predicted cancellation rate for a product for which an order has been accepted based on the user information, order information, product information, and store information, a calculation unit that calculates a predicted number of completed transactions for each product in a specified period based on the predicted cancellation rate, and a generation unit that generates a ranking of products based on the predicted number of completed transactions for each product. Effect of the Invention

[0008] According to the present disclosure, it is possible to provide an information processing device, an information processing method, and an information processing program that can suggest products to users based on rankings that reflect appropriate transaction completion predictions. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating an example of information processing according to an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating an example of the configuration of an information processing system according to the embodiment. [Diagram 3] FIG. 3 is a diagram illustrating an example of the configuration of the information processing device according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of information stored in the user information storage unit of the information processing device according to the embodiment. [Diagram 5] FIG. 5 is a diagram illustrating an example of information stored in the product information storage unit of the information processing device according to the embodiment. [Figure 6]FIG. 6 is a diagram illustrating an example of information stored in the order information storage unit of the information processing device according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of information stored in the store information storage unit of the information processing device according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating an example of information stored in the model storage unit of the information processing device according to the embodiment. [Figure 9] FIG. 9 is a diagram illustrating an example of the configuration of a user terminal according to the embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of the configuration of a carrier terminal according to the embodiment. [Figure 11] FIG. 11 is a flowchart illustrating an example of information processing according to the embodiment. [Figure 12] FIG. 12 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the information processing device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, a detailed description will be given of an information processing device, an information processing method, and an information processing program according to the present disclosure (hereinafter, referred to as an "embodiment") with reference to the drawings. Note that the information processing device, the information processing method, and the information processing program according to the present disclosure are not limited to the embodiment.

[0011] (Embodiment) 1. Information Processing According to the Embodiment 1-1. Example of information processing according to the embodiment First, an example of information processing according to the embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of information processing according to the embodiment. Fig. 1 shows an example in which information processing according to the embodiment is executed by an information processing device 100, a user terminal 200, and a business operator terminal 300. Below, the information processing according to the embodiment will be described step by step.

[0012] First, the information processing device 100 receives product information from the business operator terminal 300 (step S1). For example, the information processing device 100 receives product information from the business operator terminal 300 indicating information on products to be exhibited in a store, which is a virtual shop operated by the business operator on an electronic commerce site. The product information may include information related to items such as a product ID, a product name, product information, and a store ID. After acquiring the product information from the business operator terminal 300, the information processing device 100 stores the acquired product information in a product information storage unit 122, which will be described later.

[0013] Next, the information processing device 100 acquires user information from the user terminal 200 (step S2). Here, the user information is, for example, information about a user that is provided to an e-commerce site provider when the user uses a specific e-commerce site. For example, the information processing device 100 acquires the user information from the user terminal 200 when the user U accesses the e-commerce site using the user terminal 200. After acquiring the user information from the provider terminal 300, the information processing device 100 stores the acquired user information in the user information storage unit 121, which will be described later.

[0014] Next, the information processing device 100 receives order information from the user terminal 200 (step S3). Here, the order information is information related to a user's order for a product in an electronic commerce service. The order information may include a store ID, a user ID, a product ID, and an order quantity. For example, the information processing device 100 obtains order information received in a predetermined electronic commerce service from a user U via the user terminal 200. Note that the user U and the user terminal 200 shown in FIG. 1 are merely examples, and the information processing device 100 may obtain order information received in a predetermined electronic commerce service from the user terminals 200 of more users. After obtaining the order information from the user terminal 200, the information processing device 100 stores the obtained order information in the order information storage unit 123, which will be described later. In addition, when the information processing device 100 receives order information from the user terminal 200 and then receives a cancellation of the order, the information processing device 100 stores information indicating the date on which the order was canceled in the order information storage unit 123 in association with the order information.

[0015] Next, the information processing device 100 provides the order information to the business operator terminal 300 (step S4). For example, the information processing device 100 provides the order information to the business operator terminal 300 of the business to which the order information received in step S3 is to be placed. The business operator to which the order information has been provided to the business operator terminal 300 prepares the product according to the order information and makes arrangements for delivery to the user. Then, when the delivery of the product to the user is completed, the business operator terminal 300 transmits information indicating that the delivery of the product for which the order information was provided has been completed to the information processing device 100. When the information processing device 100 receives information indicating the date on which delivery of the product corresponding to the order information was completed from the business operator terminal 300, it stores the date on which delivery of the product was completed in association with the order information in the order information storage unit 123.

[0016] Next, the information processing device 100 predicts the predicted cancellation rate based on the user information, the order information, the product information, and the store information (step S5). For example, the information processing device 100 predicts the predicted cancellation rate using a trained model that has been machine-learned with the user information, the order information, the product information, and the store information as explanatory variables and the predicted cancellation rate as a target variable. Note that the learning model in this case may be, for example, a DNN (Deep Neural Network) or a decision tree. That is, the information processing device 100 predicts the predicted cancellation rate using a trained model that has been trained to learn the relationship between the user information, the order information, the product information, the store information, and the presence or absence of cancellation.

[0017] Next, the information processing device 100 calculates the predicted number of completed transactions for each product in a predetermined period (step S6). For example, the information processing device 100 extracts order information received for product A in a predetermined period from the order information storage unit 123. Then, the information processing device 100 calculates the predicted number of completed transactions using the predicted cancellation rate predicted for each order in step S5. Specifically, the predicted number of completed transactions is calculated by multiplying the number of orders for each order of the product by a value obtained by subtracting the predicted cancellation rate from 1. Note that, in FIG. 1, it is shown that the number of orders for order A is 2 and the predicted cancellation rate is 0%, so that the predicted number of completed transactions is calculated as 2. Note that, for products for which delivery has been completed, the number of products for which delivery has been completed is calculated as the predicted number of completed transactions without using the predicted cancellation rate.

[0018] Next, the information processing device 100 generates a ranking of the products based on the predicted number of completed transactions for each product (step S7). For example, the information processing device 100 generates a ranking of the products in descending order of the predicted number of completed transactions for each product calculated in step S6. Note that, in FIG. 1, the predicted number of completed transactions for product A is 100, the predicted number of completed transactions for product B is 88, and the predicted number of completed transactions for product C is 66, so that the ranking is generated with product A in first place, product B in second place, and product C in third place.

[0019] Next, the information processing device 100 suggests products based on the rankings (step S8). For example, the information processing device 100 may suggest products to the user terminal 200 of a user who has visited a specific electronic commerce site by providing a message suggesting to the user to purchase a product whose ranking calculated in step S7 is equal to or higher than a specific rank. For example, the information processing device 100 may provide the user terminal 200 with a message such as "Why not try purchasing product A?"

[0020] According to this, the predicted number of completed transactions in a predetermined period is calculated taking into consideration the predicted cancellation rate predicted for each order, and a ranking of products is generated based on the predicted number of completed transactions, and products can be proposed to users based on the generated ranking. Therefore, a ranking of products can be generated based on the predicted number of completed transactions reflecting the possibility of an order being canceled, and products can be proposed to users based on the ranking. Therefore, it is possible to provide an information processing device 100 that can propose products to users based on a ranking that reflects an appropriate prediction of successful transactions.

[0021] 2. Configuration of the Information Processing System Next, the configuration of the information processing system according to the embodiment will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of the configuration of the information processing system according to the embodiment. As shown in Fig. 2, the information processing system 1 according to the embodiment has an information processing device 100, a user terminal 200, a business operator terminal 300, and a network N. Below, these configurations will be briefly described in order.

[0022] The information processing device 100 may be, for example, a personal computer (PC), a work station (WS), a computer having a server function, etc. The information processing device 100 performs processing based on information transmitted from the user terminal 200 via the network N, for example.

[0023] The user terminal 200 is an information processing device used by a user. The user terminal 200 may be, for example, an information processing device such as a smartphone, a tablet terminal, a desktop PC, a notebook PC, a mobile phone, or a PDA (Personal Digital Assistant). In the example shown in FIG. 1, the user terminal 200 is a smartphone.

[0024] The business operator terminal 300 is an information processing device used by a business operator. The business operator terminal 300 may be, for example, an information processing device such as a smartphone, a tablet terminal, a desktop PC, a notebook PC, a mobile phone, a PDA, etc. In the example shown in FIG. 1, the business operator terminal 300 is a notebook PC.

[0025] The network N connects the information processing device 100, the user terminal 200, and the operator terminal 300 in a wired or wireless manner so that they can communicate with each other. If the network N is wired, it may be realized by Ethernet (registered trademark) defined in IEEE802.3. If the network N is wireless, it may be realized by a wireless LAN (Local Area Network) defined in IEEE802.11.

[0026] 3. Configuration of Information Processing Device Next, the configuration of the information processing device 100 will be described with reference to FIG.

[0027] 3 is a diagram showing an example of the configuration of an information processing device according to an embodiment. As shown in FIG. 3, the information processing device 100 includes a communication unit 110, a storage unit 120, and a control unit .

[0028] (Regarding communication unit 110) The communication unit 110 is realized by, for example, a network interface card (NIC), a wireless local area network (LAN) card, etc. The communication unit 110 is connected to a network N by wire or wirelessly, and transmits and receives information to and from the user terminal 200, etc.

[0029] (Regarding the storage unit 120) The storage unit 120 includes a main storage device and an external storage device. The main storage device stores programs executed by the control unit 130 or data processed by the control unit 130. The main storage device may be realized by, for example, a semiconductor memory element such as a random access memory (RAM), a read only memory (ROM), or a flash memory. The external storage device saves data processed by the control unit 130. The external storage device may be realized by, for example, a hard disk, a solid state drive (SSD), a magnetic tape, an optical disk, or the like.

[0030] 3, the storage unit 120 includes a user information storage unit 121, a product information storage unit 122, an order information storage unit 123, a store information storage unit 124, and a model storage unit 125. The configuration of these units will be described below in order.

[0031] (Regarding the user information storage unit 121) The user information storage unit 121 stores information about a user, that is, user information. The user information is, for example, information about a user that is provided to an information service provider when the user uses a specific information service. Here, an example of information stored in the user information storage unit 121 will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of information stored in the user information storage unit of the information processing device according to the embodiment.

[0032] In the example shown in FIG. 4, the user information storage unit 121 stores information relating to the items "user ID," "date of birth," "gender," and "occupation" in association with each other.

[0033] "User ID" is an identifier that identifies a user and is represented by a string or number, etc. "Date of birth" is information about the user's date of birth linked to the "User ID". "Gender" is information about the user's gender linked to the "User ID". "Occupation" is information about the user's occupation linked to the "User ID".

[0034] That is, FIG. 4 shows that the user identified by the user ID "UID#1" has a date of birth "date of birth #U1", a gender "female", and an occupation "occupation #U1".

[0035] In addition, the information stored in the user information storage unit 121 is not limited to information relating to the items of "user ID," "date of birth," "gender," and "occupation," and any other information related to the user may be stored.

[0036] (Regarding the product information storage unit 122) The product information storage unit 122 stores product information indicating information related to products handled in the electronic commerce service. An example of information stored in the product information storage unit 122 will now be described with reference to Fig. 5. Fig. 5 is a diagram showing an example of information stored in the product information storage unit of the information processing device according to the embodiment.

[0037] In the example shown in FIG. 5, the product information storage unit 122 stores information relating to the items "product ID", "product name", "product information", and "store ID" in association with each other.

[0038] A "product ID" is an identifier that identifies a product, and is represented by a character string or a number, etc. A "product name" is the name of the product identified by the "product ID". "Product information" is information about the product identified by the "product ID", and may be, for example, information about the type, size, features, etc. of the product. A "store ID" is an identifier that identifies the store that sells the product identified by the "product ID", and is represented by a character string or a number, etc.

[0039] That is, in FIG. 5, the product name of the product identified by the product ID "MID#1" is "PNM#1", the product information of the product is "PINF#1", and the store ID of the product is "STRID#1".

[0040] The information stored in the product information storage unit 122 is not limited to information relating to the items "Product ID", "Product Name", "Product Information", and "Store ID", and any other information relating to product information may be stored.

[0041] (Regarding the order information storage unit 123) The order information storage unit 123 stores order information indicating information related to a product order received from a user U. An example of information stored in the order information storage unit 123 will now be described with reference to Fig. 6. Fig. 6 is a diagram showing an example of information stored in the order information storage unit of the information processing device according to the embodiment.

[0042] In the example shown in FIG. 6, the order information storage unit 123 stores information relating to the items "order ID," "order date," "user ID," "product ID," "store ID," "order quantity," "whether or not canceled," and "delivery completion date" in association with each other.

[0043] The "order ID" is an identifier for identifying an order and is represented by a character string, a number, or the like. The "order date" indicates the date on which the order identified by the "order ID" was accepted. The "user ID" is an identifier for identifying the user U who placed the order identified by the "order ID" and is represented by a character string, a number, or the like. The "store ID" is an identifier for identifying the store to which the order identified by the "order ID" is placed and is represented by a character string, a number, or the like. The "product ID" is an identifier for identifying an ordered product in the order identified by the "order ID" and is represented by a character string, a number, or the like. The "order quantity" indicates the order quantity of the product in the order identified by the "order ID" and is represented by, for example, the number or weight of the ordered product. The "cancellation status" is information indicating whether the order identified by the "order ID" was canceled after the order was accepted. The "delivery completion date" indicates the date on which the product ordered in the order identified by the "order ID" was delivered to the user, and if the delivery of the ordered product is incomplete, information indicating that the delivery is incomplete is stored.

[0044] That is, in Figure 6, the order date when the order identified by the order ID "ODID#1" was placed is "ODDT#1", the user ID of user U who placed that order is "UID#1", the store ID of the store to which the order was placed is "STRID#1", the product ID of the product ordered in that order is "MID#1", the order quantity of the product ordered in that order is "ODQT#1", whether the order was canceled or not is "CL#1", and the delivery completion date of the product ordered in that order is "DVDT#1".

[0045] The information stored in the order information storage unit 123 is not limited to information relating to the items "Order ID," "Order date," "User ID," "Product ID," "Store ID," "Order quantity," "Cancellation status," and "Delivery completion date," and any other information relating to order information may be stored.

[0046] (Regarding the store information storage unit 124) The store information storage unit 124 stores store information indicating information related to a store that operates in the electronic commerce service. An example of information stored in the store information storage unit 124 will now be described with reference to Fig. 7. Fig. 7 is a diagram showing an example of information stored in the store information storage unit of the information processing device according to the embodiment.

[0047] In the example shown in FIG. 7, the store information storage unit 124 stores information relating to the items "store ID", "business ID", "business name", and "contact information" in association with each other.

[0048] "Store ID" is an identifier that identifies a store that operates an e-commerce service and is represented by a character string or a number, etc. "Business ID" is an identifier that identifies the business that operates the store identified by the "Store ID" and is represented by a character string or a number, etc. "Business name" is the name of the business identified by the "Business ID". "Contact information" is the contact information of the business identified by the "Business ID" and may be, for example, an email address or a phone number.

[0049] That is, in FIG. 7, the business ID of the business operating the store identified by the store ID "STRID#1" is "MID#1", the business name of the business is "MNM#1", and the contact information of the business is "CTDT#1".

[0050] The information stored in the store information storage unit 124 is not limited to information relating to the items "Store ID," "Business ID," "Business name," and "Contact information," and any other information relating to store information may be stored.

[0051] (About the model storage unit 125) The model storage unit 125 stores information related to the machine learning model. Here, an example of information stored in the model storage unit 125 will be described with reference to Fig. 8. Fig. 8 is a diagram showing an example of information stored in the model storage unit of the information processing device according to the embodiment.

[0052] In the example shown in FIG. 8, the model storage unit 125 stores information relating to the items "model ID" and "model data" in association with each other.

[0053] "Model ID" is an identifier for identifying a machine learning model and is represented by a character string, a number, or the like. "Model data" indicates model data of the machine learning model. For example, the machine learning model may be a neural network or a decision tree.

[0054] 8, the model identified by the model ID "M#1" indicates the machine learning model M#1. Also, the model data "MDT#1" indicates the model data of the machine learning model M#1.

[0055] Here, when the machine learning model is a neural network, the model data "MDT#1" includes various information such as, for example, connection information on how the nodes included in each of the multiple layers constituting the neural network are connected to each other, and connection coefficients multiplied by the numerical values ​​input and output between the connected nodes, etc. When the machine learning model is a decision tree, the contents and levels of explanatory variables that are branching points of the decision tree are stored.

[0056] Note that the information stored in the model storage unit 125 is not limited to information relating to the items "model ID" and "model data", and any other information related to a machine learning model may be stored.

[0057] (Regarding the control unit 130) Next, returning to Fig. 3, the control unit 130 will be described. The control unit 130 is a controller that controls the information processing device 100, and is realized, for example, by a CPU (Central Processing Unit) or an MPU (Micro Processing Unit) reading out various programs stored in the storage unit 120 of the information processing device 100 and executing them using a RAM as a working area. The control unit 130 is also a controller, and may be realized, for example, by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0058] 3, the control unit 130 has an acquisition unit 131, a reception unit 132, a learning unit 133, a prediction unit 134, a calculation unit 135, a generation unit 136, and a proposal unit 137. The control unit 130 realizes these functions by reading out a program from the storage unit 120 and executing the program using the RAM as a working area, thereby realizing or executing the functions and actions of the information processing described below. These processes will be described in order below.

[0059] (Regarding the acquisition unit 131) The acquisition unit 131 acquires user information indicating information about a user from the user terminal 200. Here, the user information is, for example, information about a user that is provided to an e-commerce site provider when the user uses a specific e-commerce site. For example, the acquisition unit 131 acquires the user information from the user terminal 200 when the user U accesses the e-commerce site using the user terminal 200. After acquiring the user information from the provider terminal 300, the acquisition unit 131 stores the acquired user information in the user information storage unit 121.

[0060] (Regarding the reception unit 132) The reception unit 132 receives various information. For example, the reception unit 132 receives product information indicating information about products to be exhibited in a store, which is a virtual shop operated by a business operator on an electronic commerce site, from the business operator terminal 300. The product information may include information related to items such as a product ID, a product name, product information, and a store ID. After receiving the product information from the business operator terminal 300, the reception unit 132 stores the received product information in the product information storage unit 122.

[0061] Furthermore, the reception unit 132 receives order information indicating information related to a user's order for a product from the user terminal 200. Here, the order information is information related to a user's order for a product in an e-commerce service. The order information may include a store ID, a user ID, a product ID, and an order quantity. The reception unit 132 acquires the order information received in a predetermined e-commerce service from the user U via the user terminal 200. After acquiring the order information from the user terminal 200, the reception unit 132 stores the acquired order information in the order information storage unit 123.

[0062] (About learning unit 133) The learning unit 133 trains the model by machine learning using the user information, order information, product information, and store information as explanatory variables and the predicted cancellation rate as a target variable. Note that the learning model in this case may be, for example, a deep neural network (DNN) or a decision tree. That is, the learning unit 133 trains the model to learn the relationship between explanatory variables including the user information, order information, product information, and store information, and whether or not an order is canceled.

[0063] (Regarding the prediction unit 134) The prediction unit 134 predicts the predicted cancellation rate of the product for which an order has been accepted, based on the user information, the order information, the product information, and the store information. Specifically, the prediction unit 134 predicts the predicted cancellation rate using a trained model that has been machine-learned with the user information, the order information, the product information, and the store information as explanatory variables and the predicted cancellation rate as a target variable. Note that, for example, a DNN or a decision tree may be used as the learning model in this case. That is, the prediction unit 134 predicts the predicted cancellation rate using a trained model that has been trained to learn the relationship between the user information, the order information, the product information, the store information, and the presence or absence of cancellation.

[0064] (Regarding the calculation unit 135) The calculation unit 135 calculates the predicted number of completed transactions for each product in a predetermined period based on the predicted cancellation rate. For example, the calculation unit 135 extracts order information received for product A in a predetermined period from the order information storage unit 123. Then, the calculation unit 135 calculates the predicted number of completed transactions using the predicted cancellation rate predicted for each order by the prediction unit 134. Specifically, the calculation unit 135 calculates the predicted number of completed transactions by multiplying the number of orders for each order of the product by a value obtained by subtracting the predicted cancellation rate from 1. Note that, for products for which delivery has been completed, the calculation unit 135 calculates the number of delivered products as the predicted number of completed transactions without using the predicted cancellation rate.

[0065] (Regarding the generation unit 136) The generation unit 136 generates a ranking of products based on the predicted number of completed transactions for each product. For example, the generation unit 136 generates a ranking of products in descending order of the predicted number of completed transactions for each product calculated by the calculation unit 135. Note that the table shown below the text of step S7 in Fig. 1 shows that the predicted number of completed transactions for product A is 100, the predicted number of completed transactions for product B is 88, and the predicted number of completed transactions for product C is 66, so that the ranking is generated with product A in first place, product B in second place, and product C in third place.

[0066] (Regarding Proposal Section 137) The suggestion unit 137 suggests to the user to purchase a product whose ranking in the ranking generated by the generation unit 136 is equal to or higher than a predetermined ranking. For example, the suggestion unit 137 may suggest a product by providing a message suggesting to the user to purchase a product whose ranking in the ranking generated by the generation unit 136 is equal to or higher than a predetermined ranking, to the user terminal 200 of the user who visited a predetermined electronic commerce site. For example, the suggestion unit 137 may provide to the user terminal 200 a message such as "Why don't you buy product A?"

[0067] [4. User terminal configuration] Next, the configuration of the user terminal 200 according to the embodiment will be described with reference to Fig. 6. Fig. 6 is a diagram showing an example of the configuration of the user terminal according to the embodiment. As shown in Fig. 6, the user terminal 200 has a communication unit 210, an input unit 220, an output unit 230, and a control unit 240.

[0068] The communication unit 210 is realized by, for example, a NIC, a wireless LAN card, etc. The communication unit 210 is connected to a network N by wire or wirelessly, and transmits and receives various information to and from the information processing device 100 via the network N.

[0069] Various types of operation information are input to the input unit 220 by the user. For example, the input unit 220 may receive various operations from the user via a display surface (e.g., the output unit 230) using a touch panel. The input unit 220 may also receive various operations from buttons provided on the user terminal 200, or a keyboard or mouse connected to the user terminal 200.

[0070] The output unit 230 is a display screen of a tablet terminal or the like realized by, for example, a liquid crystal display or an organic EL (Electro-Luminescence) display, and is a display device for displaying various information. In other words, when the input unit 220 of the user terminal 200 is a touch panel, the output unit 230, which is a display screen, receives input from the user and outputs the input to the user. The output unit 230 may also be a speaker, and may output sound from the speaker.

[0071] The control unit 240 is realized, for example, by a CPU, an MPU, or the like executing various programs stored in the user terminal 200 using a RAM as a working area. The control unit 240 may also be realized, for example, by an integrated circuit such as an ASIC or an FPGA.

[0072] As shown in FIG. 6, the control unit 240 includes a receiving unit 241 and a proposing unit 242.

[0073] The reception unit 241 receives various information input from a user. For example, the reception unit 241 receives an order for a product from a user in a predetermined electronic commerce service. The product order information received from a user may include a user ID, a product ID, a store ID, and an order quantity.

[0074] The suggestion unit 242 suggests the purchase of a product to the user. For example, the suggestion unit 242 may suggest the purchase of a product to the user by causing the output unit 230 to display a message for purchasing the product provided by the suggestion unit 137 of the information processing device 100. For example, the suggestion unit 242 may cause the output unit 230 to display a message such as "Why don't you try purchasing product A" provided by the suggestion unit 137 of the information processing device 100.

[0075] [5. Configuration of carrier terminal] Next, the configuration of the operator terminal 300 according to the embodiment will be described with reference to Fig. 10. Fig. 10 is a diagram showing an example of the configuration of the operator terminal according to the embodiment. As shown in Fig. 10, the operator terminal 300 has a communication unit 310, an input unit 320, an output unit 330, and a control unit 340.

[0076] The communication unit 310 is realized by, for example, a NIC etc. The communication unit 310 is connected to a network N by wire or wirelessly, and transmits and receives various information to and from the information processing device 100 via the network N.

[0077] The input unit 320 receives various types of operation information from the business operator M. For example, the input unit 320 may receive various operations from the business operator M from a keyboard or a mouse connected to the business operator terminal 300. The input unit 320 may also receive various operations from the business operator M via a display surface (e.g., the output unit 330) of a touch panel.

[0078] The output unit 330 is a display screen realized by, for example, a liquid crystal display or an organic EL display, and is a display device for displaying various information. When the input unit 320 of the business operator terminal 300 accepts various operations from the business operator M via a touch panel, the display screen of the output unit 330 accepts input from the user U, and also outputs the input to the user U. The output unit 230 is a speaker, and may output sound from the speaker.

[0079] The control unit 340 is realized, for example, by a CPU, an MPU, or the like executing various programs stored in the operator terminal 300 using a RAM as a working area. The control unit 340 may also be realized, for example, by an integrated circuit such as an ASIC or an FPGA.

[0080] As shown in FIG. 10, the control unit 340 includes a receiving unit 341, a notifying unit 342, and a reporting unit 343.

[0081] The receiving unit 341 receives various information from the business. For example, the receiving unit 341 receives product information from the business indicating information on products to be exhibited in a store, which is a virtual shop operated by the business on an e-commerce site. The product information may include information related to items such as a product ID, a product name, product information, and a store ID.

[0082] The notification unit 342 notifies the business operator of the order information. For example, the notification unit 342 causes the output unit 330 to display the order information notified to the business operator terminal 300 of the business operator to which the order information received by the reception unit 132 of the information processing device 100 is to be placed.

[0083] The reporting unit 343 reports information to the information processing device 100 that the delivery of the ordered product to the user has been completed. The business operator, whose business operator terminal 300 has been notified of the order information by the notification unit 342, prepares the product according to the order information and makes arrangements for delivery to the user. Then, when the business operator has completed delivery of the product to the user, inputs to the business operator terminal 300 information that the delivery of the product for which order information was provided has been completed. Then, the reporting unit 343 links information indicating that the product has been delivered to the user to the order information for the product that has been delivered, and transmits the information to the information processing device 100.

[0084] [6. Information processing flow] Next, the procedure of information processing according to the embodiment will be described with reference to Fig. 11. Fig. 11 is a flowchart showing an example of information processing according to the embodiment. Below, the procedure of information processing according to the embodiment will be described with reference to the flowchart shown in Fig. 11.

[0085] First, the information processing device 100 accepts product information from the business terminal (step S101). Next, the information processing device 100 acquires user information from the user terminal (step S102). Then, the information processing device 100 accepts order information from the user terminal (step S103). Then, the information processing device 100 provides the order information to the business terminal (step S104). Then, the information processing device 100 predicts a predicted cancellation rate based on the user information, order information, product information, and store information (step S105). Then, the information processing device 100 calculates a predicted number of completed transactions for each product in a predetermined period (step S106). Then, based on the predicted number of completed transactions for each product, the information processing device 100 calculates: A ranking of the products is generated (step S107). Then, the information processing device 100 suggests products based on the ranking (step S108).

[0086] According to this, it is possible to calculate the predicted number of completed transactions in a specified period taking into consideration the predicted cancellation rate predicted for each order, generate a ranking of products based on the predicted number of completed transactions, and suggest products to users based on the generated ranking. Therefore, it is possible to generate a ranking of products based on the predicted number of completed transactions reflecting the possibility of an order being canceled, and suggest products to users based on the ranking. Therefore, it is possible to provide an information processing method and information processing program that can suggest products to users based on a ranking that reflects an appropriate transaction completion prediction.

[0087] [7. Hardware Configuration] Moreover, the information processing device 100 according to the above-described embodiment is realized by a computer 1000 having a configuration as shown in Fig. 12, for example. Fig. 12 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device. The computer 1000 is connected to an output device 1010 and an input device 1020, and has a configuration in which a calculation device 1030, a primary storage device 1040, a secondary storage device 1050, an output IF (Interface) 1060, an input IF 1070, and a network IF 1080 are connected by a bus 1090.

[0088] The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050 and programs read from the input device 1020, and executes various processes. The primary storage device 1040 is a memory device, such as a RAM, that temporarily stores data used by the arithmetic device 1030 for various calculations. The secondary storage device 1050 is a storage device that stores data used by the arithmetic device 1030 for various calculations and various databases, and is realized by a ROM (Read Only Memory), a HDD (Hard Disk Drive), a flash memory, or the like.

[0089] The output IF 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various types of information, such as a monitor or a printer, and is realized by a connector conforming to a standard such as USB (Universal Serial Bus), DVI (Digital Visual Interface), or HDMI (registered trademark) (High Definition Multimedia Interface). The input IF 1070 is an interface for receiving information from various input devices 1020, such as a mouse, keyboard, and scanner, and is realized by a USB, for example.

[0090] The input device 1020 may be a device that reads information from, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a 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. The input device 1020 may also be an external storage medium such as a USB memory.

[0091] The network IF 1080 receives data from other devices via the network N and sends it to the arithmetic device 1030, and also transmits data generated by the arithmetic device 1030 to other devices via the network N.

[0092] The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output IF 1060 and the input IF 1070. For example, the arithmetic unit 1030 loads a program from the input device 1020 or the secondary storage device 1050 onto the primary storage device 1040 and executes the loaded program.

[0093] For example, when the computer 1000 functions as the information processing device 100, the arithmetic unit 1030 of the computer 1000 realizes the functions of the control unit 130 of the information processing device 100 by executing a program loaded onto the primary storage device 1040.

[0094] 8. Composition and Effects The information processing device 100 according to the present disclosure includes an acquisition unit 131 that acquires user information indicating information about a user from a user terminal 200, a reception unit 132 that receives order information indicating information about the user's order for a product from the user terminal 200, a prediction unit 134 that predicts a predicted cancellation rate of a product for which an order has been accepted based on the user information, order information, product information, and store information, a calculation unit 135 that calculates a predicted number of completed transactions for each product in a specified period based on the predicted cancellation rate, and a generation unit 136 that generates a ranking of products based on the predicted number of completed transactions for each product.

[0095] According to this configuration, the predicted number of completed transactions in a predetermined period is calculated taking into consideration the predicted cancellation rate predicted for each order, and a ranking of products is generated based on the predicted number of completed transactions, and products can be proposed to users based on the generated ranking. Therefore, a ranking of products can be generated based on the predicted number of completed transactions reflecting the possibility of an order being canceled, and products can be proposed to users based on the ranking. Therefore, it is possible to provide an information processing device 100 that can propose products to users based on a ranking that reflects an appropriate prediction of successful transactions.

[0096] The information processing device 100 according to the present disclosure further includes a suggestion unit 137 that suggests to a user to purchase a product that is ranked at or above a predetermined rank in the ranking generated by the generation unit 136.

[0097] According to this configuration, it is possible to propose products to users for which the ranking reflecting the transaction completion prediction is equal to or higher than a predetermined ranking, thereby providing the information processing device 100 that can propose products to users based on the ranking reflecting an appropriate transaction completion prediction.

[0098] The prediction unit 134 of the information processing device 100 according to the present disclosure predicts the predicted cancellation rate using a trained model that has been machine-learned using user information, order information, product information, and store information as explanatory variables and the predicted cancellation rate as an objective variable.

[0099] According to this configuration, the predicted cancellation rate can be appropriately predicted using a machine learning model, and therefore, it is possible to provide the information processing device 100 that can suggest products to users based on a ranking that reflects an appropriate prediction of successful transaction.

[0100] The information processing method of the present disclosure includes the steps of acquiring user information indicating information about a user from a user terminal 200, accepting order information indicating information regarding the user's order for a product from the user terminal 200, predicting a predicted cancellation rate for the product for which orders have been accepted based on the user information, order information, product information, and store information, calculating a predicted number of completed transactions for each product in a specified period based on the predicted cancellation rate, and generating a ranking of products based on the predicted number of completed transactions for each product.

[0101] According to this configuration, it is possible to calculate the predicted number of completed transactions in a predetermined period in consideration of the predicted cancellation rate predicted for each order, generate a ranking of products based on the predicted number of completed transactions, and suggest products to users based on the generated ranking. Therefore, it is possible to generate a ranking of products based on the predicted number of completed transactions reflecting the possibility of an order being canceled, and suggest products to users based on the ranking. Therefore, it is possible to provide an information processing method that can suggest products to users based on a ranking that reflects an appropriate prediction of successful transactions.

[0102] The information processing program of the present disclosure causes a computer to execute the steps of acquiring user information indicating information about a user from a user terminal 200; accepting order information indicating information regarding the user's order for a product from the user terminal 200; predicting a predicted cancellation rate for the product for which orders have been accepted based on the user information, order information, product information, and store information; calculating a predicted number of completed transactions for each product in a specified period based on the predicted cancellation rate; and generating a ranking of products based on the predicted number of completed transactions for each product.

[0103] According to this configuration, it is possible to calculate the predicted number of completed transactions in a predetermined period in consideration of the predicted cancellation rate predicted for each order, generate a ranking of products based on the predicted number of completed transactions, and suggest products to users based on the generated ranking. Therefore, it is possible to generate a ranking of products based on the predicted number of completed transactions reflecting the possibility of an order being canceled, and suggest products to users based on the ranking. Therefore, it is possible to provide an information processing program that can suggest products to users based on a ranking that reflects an appropriate prediction of successful transactions.

[0104] The above describes the embodiment of the present application in detail based on the drawings, but this is merely an example, and the present invention can be implemented in other forms that include various modifications and improvements based on the knowledge of those skilled in the art, including the forms described in the Disclosure of the Invention section.

[0105] Moreover, the above-mentioned "section, module, unit" can be read as "means" or "circuit", etc. For example, the acquisition unit 131 can be read as an acquisition means or an acquisition circuit. [Explanation of symbols]

[0106] 1. Information Processing Systems 100 Information processing device 110 Communications Department 120 Storage section 121 User information storage unit 122 Product information storage section 123 Order information storage unit 124 Store information storage unit 125 Model Memory Section 130 Control section 131 Acquisition Department 132 Reception 133 Learning Department 134 Prediction Department 135 Calculation Section 136 Generation part 137 Proposal Department 200 User terminals 210 Communications Department 220 Input section 230 Output section 240 Control Unit 241 Reception 242 Proposal Department 300 Operator terminal 310 Communications Department 320 Input section 330 Output section 340 Control Unit 341 Reception 342 Notification Department 343 Reporting Department N Network

Claims

1. an acquisition unit that acquires user information indicating information about a user from a user terminal; A reception unit that receives order information indicating information related to a user's order for a product from a user terminal; A prediction unit that predicts a predicted cancellation rate of an ordered product based on user information, order information, product information, and store information; a calculation unit that calculates a predicted number of completed transactions for each product in a predetermined period based on a predicted cancellation rate; a generating unit that generates a ranking of products based on a predicted number of successful transactions for each product; An information processing device comprising:

2. A suggestion unit that suggests to a user to purchase a product that is ranked at or above a predetermined rank in the ranking generated by the generation unit. The information processing device according to claim 1 .

3. The prediction unit predicts the predicted cancellation rate using a trained model that has been machine-learned using user information, order information, product information, and store information as explanatory variables and the predicted cancellation rate as an objective variable. The information processing device according to claim 2 .

4. acquiring user information indicating information about a user from a user terminal; receiving order information indicating information related to a user's order for a product from a user terminal; A step of predicting a predicted cancellation rate of the ordered product based on user information, order information, product information, and store information; calculating a predicted number of transactions to be concluded for each product in a predetermined period based on the predicted cancellation rate; generating a ranking of the products based on a predicted number of successful transactions for each product; An information processing method comprising:

5. acquiring user information indicating information about a user from a user terminal; receiving order information indicating information related to a user's order for a product from a user terminal; A step of predicting a predicted cancellation rate of the ordered product based on user information, order information, product information, and store information; calculating a predicted number of transactions to be concluded for each product in a predetermined period based on the predicted cancellation rate; generating a ranking of the products based on a predicted number of successful transactions for each product; An information processing program that causes a computer to execute the following.

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