Information providing apparatus, information providing method, and information providing program

Abstract

To provide an information providing apparatus capable of providing useful information to a user, an information providing method, and an information providing program.SOLUTION: An information providing apparatus 10 includes a collection unit, a calculation unit, and a generation unit. The collection unit collects (S1) user information on a user, and collects (S2) transaction target information including a transaction target attribute. The calculation unit calculates (S3, S4), on the basis of the user information, for each transaction target, a possession score indicating a probability that the user possesses the transaction target and a purchase trend score indicating a possibility that the user purchases the transaction target. The generation unit generates (S5), on the basis of the possession score and the purchase trend score, provision information to be provided to the user.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an information providing apparatus, an information providing method, and an information providing program.

Background Art

[0002] In recent years, with the remarkable spread of the Internet, technologies related to analysis using various information on the Internet have been provided. For example, a technique for analyzing customers (appropriately, "users") in consideration of trends over time using purchase history data and the like is known.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, the above-described conventional technologies have room for improvement in providing useful information to users.

[0005] The present application has been made in view of the above, and an object thereof is to provide an information providing apparatus, an information providing method, and an information providing program that can provide useful information to a user.

Means for Solving the Problems

[0006] In order to solve the above-described problems and achieve the object, an information providing apparatus according to the present invention includes a collection unit that collects user information regarding a user, and based on the user information, for each transaction target, an ownership score indicating the possibility that the user owns it, and a purchase tendency score indicating the possibility that the user purchases it, and a calculation unit that calculates a purchase tendency score, and a generation unit that generates provision information to be provided to the user based on the ownership score and the purchase tendency score.

[0007] Also, the information providing method according to the present invention is an information providing method executed by an information providing apparatus, including a collecting step of collecting user information regarding a user, and based on the user information, for each transaction target, calculating an ownership score indicating the possibility that the user owns it, and a purchase tendency score indicating the possibility that the user purchases it; and a generating step of generating providing information to be provided to the user based on the ownership score and the purchase tendency score.

[0008] Also, the information providing program according to the present invention causes a computer to execute a collecting procedure for collecting user information regarding a user, a calculating procedure for calculating, for each transaction target, an ownership score indicating the possibility that the user owns it and a purchase tendency score indicating the possibility that the user purchases it based on the user information, and a generating procedure for generating providing information to be provided to the user based on the ownership score and the purchase tendency score.

Effect of the Invention

[0009] In the present invention, useful information can be provided to the user.

Brief Description of the Drawings

[0010]

Figure 1

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[0011] Hereinafter, embodiments for implementing the information providing apparatus, information providing method, and information providing program according to the present application (hereinafter, embodiments) will be described in detail with reference to the drawings. Note that the information providing apparatus, information providing method, and information providing program according to the present application are not limited by this embodiment. Also, in the following embodiments, the same parts are denoted by the same reference numerals, and redundant explanations are omitted.

[0012] 〔Embodiment〕 Hereinafter, the processing of the information providing system 100 according to the embodiment, the configuration of the information providing apparatus 10, specific examples of the score calculation process, and the flow of the information providing process will be described in order, and finally the effects of this embodiment will be described.

[0013] [1. Processing of Information Providing System 100] The processing of the information providing system (appropriately, this system) 100 according to the present embodiment will be described with reference to FIG. 1. FIG. 1 is a diagram showing an example of the processing of the information providing system according to the embodiment. Hereinafter, the configuration example of this system 100, the processing of this system 100, and the effects of this system 100 will be described in order.

[0014] (1-1. Configuration Example of System 100) The information providing system 100 shown in FIG. 1 includes an information providing device 10, a user terminal 20, and a transaction target information database 30. Here, the information providing device 10, the user terminal 20, and the transaction target information database 30 are communicably connected by wire or wirelessly via a predetermined communication network (not shown). Note that the system 100 may include a plurality of information providing devices 10, a plurality of user terminals 20, and a plurality of transaction target information databases 30.

[0015] (1-1-1. Information providing device 10) The information providing device 10 is a device that transmits and receives data between the user terminal 20 and the transaction target information database 30, and is realized by, for example, a server device, a cloud system, or the like. In the example of FIG. 1, the case where the information providing device 10 is realized by a server device is shown.

[0016] (1-1-2. User terminal 20) The user terminal 20 is a device (computer) used by a user U who browses a web page or conducts Internet shopping or the like on the web. The user terminal 20 receives operations by the user U. Note that the user terminal 20 is realized by, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like. In the example of FIG. 1, the case where the user terminal 20 is realized by a smartphone is shown.

[0017] (1-2. Processing of system 100) (1-2-1. Processing of step S1) In this system 100, first, the information providing device 10 collects user information of the user U from the user terminal 20 (step S1). Here, the user information is information regarding the user U of this system 100, including, in addition to the user U's behavior history such as the search history and purchase history on the user U's website, user attributes such as the user U's gender, age, age group, occupation, annual income, place of residence, marital status (married / unmarried), and presence or absence of children. Further, the user information may include information such as the screen information of the user terminal 20, the location information and biometric information of the user U, and is not particularly limited. Also, in the example of FIG. 1, the information providing device 10 acquires the user information of the user U from the user terminal 20, but it may also be acquired from a terminal of the user U (not shown), other terminals / databases, etc.

[0018] (1-2-2. Processing of step S2) In this system 100, second, the information providing device 10 refers to the transaction target information database 30 and acquires transaction target information (step S2). Here, the transaction target information is information regarding transaction targets such as products and services, including transaction target attributes such as product names, service names, classifications / types of products and services, business names, offering prices, warranty periods, and expiration dates, but is not particularly limited. Also, the transaction targets include not only targets for paid purchases and uses but also targets provided free of charge in response to customer requests. Also, in the example of FIG. 1, the information providing device 10 acquires the transaction target information from the transaction target information database 30, but it may also be acquired from the user terminal 20 or other terminals / databases (not shown).

[0019] (1-2-3. Processing of step S3) In this system 100, thirdly, the information providing device 10 calculates an ownership score for each transaction target of the user U from the collected user information and transaction target information (step S3). For example, based on the user attributes, search history, purchase history, etc. of the user U, the information providing device 10 uses a machine learning model to calculate an ownership score that takes a numerical value from 0 to 1 according to the likelihood that the user U owns each transaction target. For example, when the user information of the user U and the transaction target information are input, the information providing device 10 uses a machine learning model such as a DNN (Deep Neural Network) that has been trained to output an ownership score for each transaction target of the user U to calculate the ownership score. Also, the information providing device 10 may calculate the ownership score based on rules.

[0020] (1-2-4. Processing of step S4) In this system 100, fourthly, the information providing device 10 calculates a purchase tendency score for each transaction target of the user U from the collected user information and transaction target information (step S3). For example, based on the user attributes, search history, purchase history, etc. of the user U, the information providing device 10 uses a machine learning model to calculate a purchase tendency score that takes a numerical value from 0 to 1 according to the likelihood that the user U purchases each transaction target. For example, when the user information of the user U and the transaction target information are input, the information providing device 10 uses a machine learning model such as a DNN that has been trained to output a purchase tendency score for each transaction target of the user U to calculate the purchase tendency score. Also, the information providing device 10 may calculate the purchase tendency score based on rules.

[0021] Hereinafter, specific examples will be used to explain the above-mentioned steps S3 to S4. For example, when the information providing device 10 has a purchase history of product X for the user U or a search history of the user U possessing product X (e.g., "How to use product X"), a high ownership score is calculated for product X.

[0022] The information providing device 10 calculates a purchase tendency score using common parameters such as the purchase history of related product Y of product X, transaction target attributes, user attributes, and purchase history of attributes (purchase history of other users with common user attributes). For example, when product X is a one-of-a-kind product as a transaction target attribute of product X, the information providing device 10 calculates a high purchase tendency score for product X. Also, when product X is a consumer good as a transaction target attribute of product X, the information providing device 10 calculates a low purchase tendency score for product X. Further, for example, when user U has a collecting habit as a user attribute of user U, the information providing device 10 calculates a high purchase tendency score for product X. Also, when the period of having an interest in a specific category is long (e.g., 10 years of camping experience) as a user attribute of user U, the information providing device 10 calculates a high purchase tendency score for related products, upward compatible products Y and Z of the already purchased product X.

[0023] When the information providing device 10 has no purchase history of product X for user U and has a search history (e.g., "product X review") in which user U is considering product X, it calculates a low ownership score and a high purchase tendency score for product X. On the other hand, when the information providing device 10 has neither a purchase history of product X for user U nor a search history in which user U is considering product X, it calculates a low ownership score and a low purchase tendency score for product X.

[0024] (1-2-5. Processing of step S5) Fifthly, in this system 100, the information providing device 10 generates provision information to be provided to user U based on the calculated ownership score and purchase tendency score of user U (step S5). Here, the provision information is information for inducing the purchase behavior of the transaction target, and is not particularly limited, such as an advertisement recommending a transaction target that user U does not own, an advertisement recommending a transaction target having relevance to a transaction target that user U owns, content such as a video for enhancing the purchase tendency of user U, and an advertisement recommending the transaction target at the time of replacement of the transaction target that user U owns.

[0025] For example, when the ownership score of user U for product X is low and the purchase tendency score is high (first classification), the information providing device 10 generates an advertisement for recommending product X because user U has a high possibility of purchasing product X. Also, when the ownership score of user U for product X is high and the purchase tendency score is high (second classification), the information providing device 10 generates an advertisement for recommending related product Y of product X because user U has a high possibility of purchasing a related product of product X. Further, when the ownership score of user U for product X is low and the purchase tendency score is low (third classification), the information providing device 10 does not generate an advertisement for recommending product X because user U has a low possibility of purchasing product X, but generates video content for enhancing the purchase tendency of user U. Also, when the ownership score of user U for product X is high and the purchase tendency score is low (fourth classification), the information providing device 10 generates an advertisement for recommending product X to user U at the time of replacement because user U has a high possibility of repurchasing product X in the future.

[0026] (1-2-6. Processing of step S6) In this system 100, sixthly, the information providing device 10 transmits the generated provided information to the user terminal 20 of user U (step S6). Explaining using the example of step S5 above, the information providing device 10 transmits an advertisement for recommending product X to user U of the first classification. Also, the information providing device 10 transmits an advertisement for recommending related product Y to user U of the second classification. Further, the information providing device 10 does not transmit an advertisement for recommending product X to user U of the third classification, but transmits video content for enhancing the purchase tendency of user U. Also, the information providing device 10 transmits an advertisement for recommending product X to user U of the fourth classification at the time of replacement of product X.

[0027] (1-2-7. Processing of step S7) In this system 100, seventhly, the information providing device 10 learns the calculated score (step S7). For example, when information indicating that the user U actually owns the product X is input to the machine learning model, the information providing device 10 learns using backpropagation or the like so as to output the ownership score of the product X of the user U as "1". Further, when information indicating that the user U does not own the product X is input to the machine learning model, the information providing device 10 learns using backpropagation or the like so as to output the ownership score of the product X of the user U as "0". Similarly, when information of the user U who has actually purchased the product X and information immediately before the purchase is input to the machine learning model, the information providing device 10 learns using backpropagation or the like so as to output the purchase tendency score of the product X of the user U as "1". Further, when information of the user U who has not purchased the product X is input to the machine learning model, the information providing device 10 learns using backpropagation or the like so as to output the purchase tendency score of the product X of the user U as "0".

[0028] (1-3. Effects of System 100) In this system 100, the information providing device 10 collects user information regarding the user U via the user terminal 20, and based on the collected user information of the user U, for each transaction target, calculates an ownership score indicating the possibility that the user U owns it and a purchase tendency score indicating the possibility that the user U will purchase it, and generates provision information to be provided to the user U based on the calculated ownership score and purchase tendency score. That is, this system 100 can estimate the owned products and the purchase tendency for the products based on the search behavior and purchase behavior of the user U and the user attributes of the user U, and can grasp a more detailed user profile. For example, in this system 100, it is possible to make an estimation such that the user U who owns product X also owns product Y and has a high purchase tendency. Therefore, in this system 100, it is possible to provide useful information such as advertisements for products that the user U is more likely to want to purchase to the user U. Also, in this system 100, by calculating not only the ownership score of the user U but also the purchase tendency score, it is possible to perform estimation with higher accuracy than the ownership estimation using the prior art.

[0029] Also, in this system 100, the user U is classified using the ownership score and the purchase tendency score, and information based on the classification is provided to the user U. Therefore, this system 100 can be expected to improve the targeting accuracy and contribute to an increase in advertising sales to business operators who provide transaction targets such as products and services.

[0030] Furthermore, this system 100 can also grasp information regarding regions where the purchase tendency for each product is high or low by classifying the user U using the ownership score and the purchase tendency score. For example, this system 100 also contributes to the optimization of inventory management such as being able to replenish the inventory at a mass retailer in a region where there are many customers who are interested in product X but do not own it.

[0031] [2. Configuration of Information Providing Device 10] With reference to FIG. 2, the configuration of the information providing apparatus 10 according to the embodiment will be described. FIG. 2 is a block diagram showing a configuration example of the information providing apparatus 10 according to the embodiment. As shown in FIG. 2, the information providing apparatus 10 includes a communication unit 11, a storage unit 12, and a control unit 13. Note that the information providing apparatus 10 may include an input unit (e.g., a keyboard, a mouse, etc.) for receiving various operations from an administrator or the like of the information providing apparatus 10, and a display unit (e.g., a liquid crystal display, etc.) for displaying various information.

[0032] (2-1. Communication Unit 11) The communication unit 11 is realized by, for example, a NIC (Network Interface Card) or the like. Then, the communication unit 11 is connected to a predetermined communication network (network) by wire or wirelessly, and transmits and receives information to and from various devices.

[0033] (2-2. Storage Unit 12) The storage unit 12 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. As shown in FIG. 2, the storage unit 12 according to the embodiment includes a user information storage unit 12a, a transaction target information storage unit 12b, a score information storage unit 12c, and a provided information storage unit 12d. Then, the storage unit 12 stores various information referred to when the control unit 13 operates, and various information acquired when the control unit 13 operates.

[0034] (2-2-1. User Information Storage Unit 12a) The user information storage unit 12a stores various information (user information) regarding the user U. Here, with reference to FIG. 3, an example of the information stored in the user information storage unit 12a will be described. FIG. 3 is a diagram showing an example of the user information storage unit 12a according to the embodiment. In the example of FIG. 3, the user information storage unit 12a has items such as "user ID", "user attribute", "search history", and "purchase history".

[0035] "User ID" indicates identification information for identifying user U. "User attributes" indicate information contributing to the classification of user U, such as information on the gender, age, age group, occupation, annual income, place of residence, marital status (married or unmarried), presence or absence of children, etc. of user U. Also, "user attributes" may be information indicating categories of interest inferred from products, services, etc. owned by user U. "Search history" indicates information such as search words searched by user U on the website using user terminal 20, as well as the browsing history of the website. "Purchase history" indicates information related to products, services, etc. purchased by user U on the website using user terminal 20.

[0036] That is, in Figure 3, an example is shown where, for user U identified by user ID "UID#1", the user attributes are "user attributes #1", the search history is "search history #1", and the purchase history is "purchase history #1".

[0037] (2-2-2. Transaction Target Information Storage Unit 12b) The transaction target information storage unit 12b stores various types of information (transaction target information) related to transaction targets such as products and services. Here, using Figure 4, an example of the information stored in the transaction target information storage unit 12b will be described. Figure 4 is a diagram showing an example of the transaction target information storage unit 12b according to the embodiment. In the example of Figure 4, the transaction target information storage unit 12b has items such as "transaction target ID" and "transaction target attributes".

[0038] "Transaction target ID" indicates identification information for identifying the transaction target. "Transaction target attributes" indicate information contributing to the classification of the transaction target, such as information on the product name, service name, classification / type of the product or service, business operator name, offered price, warranty period, expiration date, etc.

[0039] That is, in Figure 4, an example is shown where, for the transaction target identified by transaction target ID "GID#1", the transaction target attributes are "transaction target attributes #1".

[0040] (2-2-3. Score Information Storage Unit 12c) The score information storage unit 12c stores the ownership score and purchase tendency score (score information) for each transaction target calculated by the calculation unit 13b of the control unit 13. Here, an example of the information stored in the score information storage unit 12c will be described with reference to FIG. 5. FIG. 5 is a diagram showing an example of the score information storage unit 12c according to the embodiment. In the example of FIG. 5, the score information storage unit 12c has items such as "user ID", "transaction target ID", "ownership score", and "purchase tendency score".

[0041] The "user ID" indicates identification information for identifying the user U. The "transaction target ID" indicates identification information for identifying the transaction target. The "ownership score" is a numerical value indicating the possibility of the presence or absence of ownership and usage rights for each product or service of the user U. The "purchase tendency score" is a numerical value indicating the possibility of future purchase behavior for each product or service of the user U.

[0042] That is, in FIG. 5, for the user U identified by the user ID "UID#1", the ownership score of the transaction target identified by the transaction target ID "GID#1" is "ownership score #1", and the purchase tendency score is "purchase tendency score #1", and an example is shown where the ownership score of the transaction target identified by the transaction target ID "GID#2" is "ownership score #2" and the purchase tendency score is "purchase tendency score #2".

[0043] (2-2-4. Provided Information Storage Unit 12d) The provided information storage unit 12d stores the provided information generated by the generation unit 13c of the control unit 13. Here, an example of the information stored in the provided information storage unit 12d will be described with reference to FIG. 6. FIG. 6 is a diagram showing an example of the provided information storage unit 12d according to the embodiment. In the example of FIG. 6, the provided information storage unit 12d has items such as "user ID", "transaction target ID", and "provided information".

[0044] The "user ID" indicates identification information for identifying the user U. The "transaction target ID" indicates identification information for identifying the transaction target. The "provided information" is information including advertisements, contents, etc. selected for each product or service of the user U.

[0045] That is, in FIG. 6, an example is shown in which, for user U identified by user ID "UID#1", the provided information of the transaction target identified by transaction target ID "GID#1" is "Provided Information #1", and the provided information of the transaction target identified by transaction target ID "GID#2" is "Provided Information #2".

[0046] (2-3. Control Unit 13) The control unit 13 is realized, for example, when various programs (corresponding to an example of an information processing program) stored in a storage device inside the information providing apparatus 10 are executed with the RAM as a work area by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. Further, the control unit 13 is realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0047] As shown in FIG. 2, the control unit 13 includes a collection unit 13a, a calculation unit 13b, a generation unit 13c, a transmission unit 13d, and a learning unit 13e, and realizes or executes the functions and operations of information processing described below. Note that the internal configuration of the control unit 13 is not limited to the configuration shown in FIG. 2, and may be any other configuration as long as it can perform the information processing described later. Also, the connection relationship of each processing unit included in the control unit 13 is not limited to the connection relationship shown in FIG. 2, and may be any other connection relationship.

[0048] (2-3-1. Collection Unit 13a) The collection unit 13a collects user information regarding the user U. For example, the collection unit 13a collects, as user information, the user attributes, search history, purchase history, etc. of the user U from the user terminal 20 of the user U. Further, the collection unit 13a collects transaction target information including the transaction target attributes. For example, the collection unit 13a collects transaction target attributes, etc. from the transaction target information database 30. Note that the collection unit 13a stores the collected user information in the user information storage unit 12a. Also, the collection unit 13a stores the collected transaction target information in the transaction target information storage unit 12b.

[0049] (2-3-2. Calculation unit 13b) Based on the user information, the calculation unit 13b calculates, for each transaction target, an ownership score indicating the possibility that the user U owns it and a purchase tendency score indicating the possibility that the user U purchases it. For example, the calculation unit 13b uses, as user information, the attributes, search history, or purchase history of the user U to calculate the ownership score and the purchase tendency score for each transaction target. Also, the calculation unit 13b uses the user information and the transaction target information to calculate the ownership score and the purchase tendency score for each transaction target. To explain with a specific example, when calculating the ownership score and the purchase tendency score of the user U for the product X, the calculation unit 13b uses common parameters (purchase history of related product Y of product X, transaction target attributes, user attributes, purchase history of attributes, etc.), the search history of product X, and the purchase history of product X to calculate each score.

[0050] Regarding the calculation method, the calculation unit 13b uses a machine learning model such as a DNN that has been learned to output the ownership score and the purchase tendency score for each transaction target of the user U when the user information and the transaction target information of the user U are input, to calculate the ownership score and the purchase tendency score. Also, the information providing device 10 may calculate the ownership score and the purchase tendency score based on rules.

[0051] Regarding the calculation of the score, the calculation unit 13b calculates an ownership score and a purchase tendency score so as to take a numerical value from 0 to 1 according to the possibility for each transaction target. Further, the calculation unit 13b may calculate the ownership score and the purchase tendency score so as to take a numerical value from 0 to 100% according to the possibility for each transaction target, and the range and unit of the score to be calculated are not particularly limited.

[0052] Note that the calculation unit 13b acquires user information from the user information storage unit 12a. Further, the calculation unit 13b acquires transaction target information from the transaction target information storage unit 12b. On the other hand, the calculation unit 13b stores the calculated ownership score and purchase tendency score in the score information storage unit 12c.

[0053] (2-3-3. Generation unit 13c) The generation unit 13c generates provision information to be provided to the user U based on the ownership score and the purchase tendency score. For example, when the ownership score of the transaction target is less than a predetermined threshold and the purchase tendency score is equal to or more than the predetermined threshold (first classification), the generation unit 13c generates provision information for recommending the purchase of the transaction target. To explain using a specific example, in the case of product X, when the ownership score of the user U is lower than the set value (for example, 0.8), the purchase tendency score is higher than the set value (for example, 0.5), and the user U is in the first classification (high possibility of purchasing product X), an advertisement recommending product X is generated as the provision information for the user U.

[0054] When the ownership score of the transaction target is equal to or more than a predetermined threshold and the purchase tendency score is equal to or more than the predetermined threshold (second classification), the generation unit 13c generates provision information for recommending the purchase of the transaction target related to the transaction target. To explain using a specific example, in the case of product X, when the ownership score of the user U is higher than the set value (for example, 0.8), the purchase tendency score is higher than the set value (for example, 0.5), and the user U is in the second classification (high possibility of purchasing related product Y), an advertisement recommending related product Y is generated as the provision information for the user U.

[0055] When the ownership score of the transaction target is less than a predetermined threshold and the purchase tendency score is less than a predetermined threshold (third classification), the generation unit 13c generates provision information aimed at increasing the purchase tendency score of the transaction target. To explain using a specific example, in the case of product X, when the ownership score of user U is lower than the set value (e.g., 0.8), the purchase tendency score is lower than the set value (e.g., 0.5), and user U is in the third classification (low possibility of purchasing product X), an advertisement recommending product X is not generated, and video content for increasing the purchase tendency of user U is generated as provision information for user U.

[0056] When the ownership score of the transaction target is equal to or higher than a predetermined threshold and the purchase tendency score is less than a predetermined threshold (fourth classification), the generation unit 13c generates provision information recommending the purchase of the transaction target to be provided at the time of replacement purchase of the transaction target. To explain using a specific example, in the case of product X, when the ownership score of user U is higher than the set value (e.g., 0.8), the purchase tendency score is lower than the set value (e.g., 0.5), and user U is in the fourth classification (high possibility of repurchasing product X in the future), an advertisement recommending product X at the time of replacement purchase is generated as provision information for user U.

[0057] Note that the generation unit 13c acquires the ownership score and the purchase tendency score from the score information storage unit 12c. Also, the generation unit 13c stores the generated provision information in the provision information storage unit 12d.

[0058] (2-3-4. Transmission unit 13d) The transmission unit 13d transmits the provided information generated by the generation unit 13c to the user U. To explain using a specific example, the transmission unit 13d transmits an advertisement recommending product X to the user U in the first classification (high possibility of purchasing product X). Also, the transmission unit 13d transmits an advertisement recommending related product Y to the user U in the second classification (high possibility of purchasing related product Y). Further, the transmission unit 13d does not transmit an advertisement recommending product X to the user U in the third classification (low possibility of purchasing product X), but transmits video content for enhancing the user U's purchasing tendency. Additionally, the transmission unit 13d transmits an advertisement recommending product X at the replacement time of product X to the user U in the fourth classification (high possibility of repurchasing product X in the future).

[0059] Note that the transmission unit 13d acquires the provided information from the provided information storage unit 12d. Also, the transmission unit 13d may acquire the ownership score and the purchasing tendency score from the score information storage unit 12c and transmit them to a merchant terminal or a database (not shown).

[0060] (2-3-5. Learning Unit 13e) When the learning unit 13e is input with the user information of the user U and the transaction target information, it performs learning of a machine learning model so as to output the ownership score and the purchase tendency score for each transaction target of the user U. To explain using a specific example, when information indicating that the user U actually owns the product X is input to the machine learning model, the learning unit 13e outputs the ownership score of the product X of the user U as "1", and when information indicating that the user U does not own the product X is input to the machine learning model, the learning unit 13e performs learning of the machine learning model so as to output the ownership score of the product X of the user U as "0". Further, when information of the user U who has actually purchased the product X and information immediately before the purchase are input to the machine learning model, the learning unit 13e outputs the purchase tendency score of the product X of the user U as "1", and when information of the user U who has not purchased the product X is input to the machine learning model, the learning unit 13e performs learning of the machine learning model so as to output the purchase tendency score of the product X of the user U as "0". At this time, the learning unit 13e may perform learning of the machine learning model by backpropagation or the like.

[0061] [3. Specific Example of Score Calculation Processing] Using FIGS. 7 to 9, a specific example of the score calculation processing according to the embodiment will be described in detail. FIGS. 7 to 9 are diagrams showing a specific example of the process control system according to the embodiment.

[0062] (3-1. Specific Example 1) Using FIG. 7, a specific example 1 of the score calculation processing will be described. Hereinafter, a description will be given of a user U1 (in his / her 30s, male, single, in a technical position, annual income ○○○) who has a purchase history of the smartphone G11.

[0063] As shown in FIG. 7, the information providing apparatus 10 calculates the ownership score and purchase tendency score of the user U1 for the smartphone G11 [ownership score: 1, purchase tendency score: 0.45], earphone G12 [ownership score: 0.62, purchase tendency score: 0.32], electronic pen G13 [ownership score: 0.34, purchase tendency score: 0.32], smartwatch G14 [ownership score: 0.75, purchase tendency score: 0.35], cosmetics G15 [ownership score: 0.02, purchase tendency score: 0.12], bag G16 [ownership score: 0.31, purchase tendency score: 0.81], shirt G17 [ownership score: 0.33, purchase tendency score: 0.62], ···, glasses G18 [ownership score: 0.21, purchase tendency score: 0.71].

[0064] As shown in the above Specific Example 1, the information providing apparatus 10 estimates that the user U1 owns the smartphone G11 (ownership score of the smartphone G11 is "1") by using the purchase history of the user U1. Further, the information providing apparatus 10 estimates that the probability that the user U1 who owns the smartphone G11 owns the earphone G12 or the like is high (ownership score of the earphone G12 is "0.62") by using the purchase history and user attributes. Furthermore, when the user U1 performs a search action such as "recommended earphone", the information providing apparatus 10 can estimate that the probability of owning the earphone G12 is low by using the search history.

[0065] (3-2. Specific Example 2) With reference to FIG. 8, a specific example 2 of the score calculation process will be described. Hereinafter, a description will be given of the user U2 (in her 20s, female, married, annual income △△△) who has a purchase history of the A company smartphone G21 and has purchased the A company earphone G22 and the A company power cable G23.

[0066] As shown in FIG. 8, the information providing apparatus 10 calculates, as the ownership score and purchase tendency score of the user U2, for the Company A smartphone G21 [ownership score: 1, purchase tendency score: 0.45], Company A earphone G22 [ownership score: 1, purchase tendency score: 0.32], Company A power cable G23 [ownership score: 1, purchase tendency score: 0.32], Company A smartwatch G24 [ownership score: 0.75, purchase tendency score: 0.95], Company B smartwatch G25 [ownership score: 0.75, purchase tendency score: 0.22], and Company C smartwatch G26 [ownership score: 0.75, purchase tendency score: 0.31].

[0067] As shown in the above Specific Example 2, the information providing apparatus 10 can presume that the user U2 owns the Company A smartphone G21, Company A earphone G22, and Company A power cable G23 (ownership score of "1" for the Company A smartphone G21, Company A earphone G22, and Company A power cable G23) by using the purchase history, etc. of the user U2. Further, the information providing apparatus 10 collects information that the user "likes Company A products" as a category of interest from the purchase history of products such as the smartphone G21 manufactured by Company A. Furthermore, by using the purchase history, the information providing apparatus 10 can presume that the user U2 has a tendency to purchase genuine Company A products also for the smartwatch from the tendency to purchase genuine Company A products as peripheral devices (earphones, power cables) for the smartphone (purchase tendency score of "0.95" for the Company A smartwatch G24).

[0068] (3-3. Specific Example 3) A specific example 3 of the score calculation process will be described with reference to FIG. 9. Hereinafter, a description will be given of a user U3 (in his 40s, male, married, annual income ×××) who has a purchase history of a camping stove G31, which is camping equipment, and who has purchased and holds multiple items such as hiking boots G33 and hiking wear G34, which are hiking equipment, in the past.

[0069] As shown in FIG. 9, the information providing apparatus 10 calculates, as the ownership score and purchase tendency score of the user U3, a brazier G31 [ownership score: 1, purchase tendency score: 0.45], a tent G32 [ownership score: 0.34, purchase tendency score: 0.99], hiking boots G33 [ownership score: 1, purchase tendency score: 0.95], hiking wear G34 [ownership score: 1, purchase tendency score: 0.22], ···.

[0070] As shown in the above specific example 3, the information providing apparatus 10 can estimate that the user U3 owns a brazier G31, hiking boots G33, hiking wear G34, etc. (ownership scores of "1" for the brazier G31, hiking boots G33, and hiking wear G34) by using the purchase history and the like of the user U3. Further, by using transaction target information and the like, the information providing apparatus 10 can also estimate that the user U3 has a high affinity for mountain climbing and camping and owns a plurality of mountain climbing goods, so the user U3 has a high tendency to purchase a tent G32 (purchase tendency score of "0.99" for the tent G32).

[0071] [4. Flow of Information Providing Process] The procedure of information processing of the information providing apparatus 10 according to the embodiment will be described with reference to FIG. 10. FIG. 10 is a flowchart showing an example of the flow of the information providing process according to the embodiment. Note that the following steps S101 to S107 can also be executed in a different order. Also, among the following steps S101 to S107, there may be processes that are omitted.

[0072] (4-1. User Information Collection Process) First, the collection unit 13a of the information providing apparatus 10 executes a user information collection process (step S101). For example, the collection unit 13a collects user information of the user U from the user terminal 20.

[0073] (4-2. Transaction Target Information Collection Process) Second, the collection unit 13a of the information providing apparatus 10 executes a transaction target information collection process (step S102). For example, the collection unit 13a refers to the transaction target information database 30 and acquires transaction target information.

[0074] (4-3. All Score Calculation Processing) Thirdly, the calculation unit 13b of the information providing apparatus 10 executes an all score calculation process (step S103). For example, the calculation unit 13b calculates an all score for each transaction target of the user U from the collected user information and transaction target information.

[0075] (4-4. Purchase Tendency Score Calculation Processing) Fourthly, the calculation unit 13b of the information providing apparatus 10 executes a purchase tendency score calculation process (step S104). For example, the calculation unit 13b calculates a purchase tendency score for each transaction target of the user U from the collected user information and transaction target information.

[0076] (4-5. Provided Information Generation Processing) Fifthly, the generation unit 13c of the information providing apparatus 10 executes a provided information generation process (step S105). For example, the generation unit 13c generates provided information to be provided to the user U based on the calculated all score and purchase tendency score of the user U.

[0077] (4-6. Provided Information Transmission Processing) Sixthly, the transmission unit 13d of the information providing apparatus 10 executes a provided information transmission process (step S106). For example, the transmission unit 13d transmits the generated provided information to the user terminal 20 of the user U.

[0078] (4-7. Score Calculation Learning Processing) Seventhly, the learning unit 13e of the information providing apparatus 10 executes a score calculation learning process (step S107). For example, the learning unit 13e performs learning on the calculated all score and purchase tendency score.

[0079] [5. Effects of the Embodiment] (5-1. Effect 1) In the process according to the above-described embodiment, user information regarding the user U is collected, and based on the collected user information, for each transaction target, an ownership score indicating the possibility that the user U owns it and a purchase tendency score indicating the possibility that the user U purchases it are calculated, and based on the calculated ownership score and purchase tendency score, provision information to be provided to the user U is generated. Therefore, in this process, useful information can be provided to the user U.

[0080] (5-2. Effect 2) In the process according to the above-described embodiment, as user information, the attributes, search history, or purchase history of the user U are used, and for each transaction target, an ownership score and a purchase tendency score are calculated. Therefore, in this process, more accurate and useful information can be provided to the user U.

[0081] (5-3. Effect 3) In the process according to the above-described embodiment, transaction target information including the attributes of the transaction target is further collected, and using the collected user information and transaction target information, for each transaction target, an ownership score and a purchase tendency score are calculated. Therefore, in this process, more accurate and useful information can be provided to the user U more effectively.

[0082] (5-4. Effect 4) In the process according to the above-described embodiment, when the ownership score of the transaction target is less than a predetermined threshold and the purchase tendency score is greater than or equal to the predetermined threshold, provision information for recommending the purchase of the transaction target is generated. Therefore, in this process, more accurate and useful information can be provided to the user U who has a high possibility of purchasing the transaction target more effectively.

[0083] (5-5. Effect 5) In the process according to the above-described embodiment, when the ownership score of the transaction target is greater than or equal to a predetermined threshold and the purchase tendency score is greater than or equal to the predetermined threshold, provision information for recommending the purchase of a transaction target related to the transaction target is generated. Therefore, in this process, more accurate and useful information can be provided to the user U who has a high possibility of purchasing a transaction target related to the transaction target more effectively.

[0084] (5-6. Effect 6) In the process according to the above-described embodiment, when the ownership score of the transaction target is less than a predetermined threshold and the purchase tendency score is less than a predetermined threshold, provision information is generated for the purpose of increasing the purchase tendency score of the transaction target. For this reason, in this process, more accurate and useful information can be provided more effectively to the user U who has a low possibility of purchasing the transaction target.

[0085] (5-7. Effect 7) In the process according to the present embodiment described above, when the ownership score of the transaction target is equal to or more than a predetermined threshold and the purchase tendency score is less than a predetermined threshold, provision information is generated to recommend the purchase of the transaction target to be provided at the time of replacement of the transaction target. For this reason, in this process, more accurate and useful information can be provided more effectively to the user U who has a high possibility of repurchasing the transaction target in the future.

[0086] 〔Hardware Configuration〕 Also, the information providing apparatus 10 according to the embodiment described above is realized by, for example, a computer 1000 having a configuration as shown in FIG. 11. Hereinafter, the information providing apparatus 10 will be described as an example. FIG. 11 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information providing apparatus 10. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM 1300, an HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

[0087] The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400, and controls each part. 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.

[0088] The HDD 1400 stores programs executed by the CPU 1100, data used by such programs, and the like. The communication interface 1500 receives data from other devices via a predetermined communication network N and sends it to the CPU 1100, and transmits the data generated by the CPU 1100 to other devices via the predetermined communication network N.

[0089] The CPU 1100 controls output devices such as displays and printers, and input devices such as keyboards and mice, 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 generated data to the output device via the input / output interface 1600.

[0090] 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.

[0091] For example, when the computer 1000 functions as the information providing apparatus 10 according to the embodiment, the CPU 1100 of the computer 1000 realizes the functions of the control unit 13 by executing the program loaded on the RAM 1200. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800. As another example, these programs may be acquired from other devices via a predetermined communication network.

[0092] 〔Others〕 Although the embodiments of the present application have been described above, the present invention is not limited by the contents of these embodiments. Further, the components described above include those that can be easily assumed by those skilled in the art, those that are substantially the same, and those within the so-called equivalent range. Furthermore, the above-described components can be combined as appropriate. Furthermore, various omissions, substitutions, or changes of the components can be made without departing from the gist of the above-described embodiments.

[0093] In addition, among the respective processes described in the above embodiments, all or part of the processes described as being automatically performed can also be manually performed, or all or part of the processes described as being manually performed can be automatically performed by a known method. In addition, regarding the processing procedures, specific names, and information including various data and parameters shown in the above documents and drawings, they can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

[0094] Also, each component of each device shown in the drawings is a functional concept, and it is not necessarily physically configured as shown in the drawings. That is, the specific form of the distribution and integration of each device is not limited to that shown in the drawings, and all or part of it can be functionally or physically distributed and integrated in any unit according to various loads and usage situations.

[0095] For example, the above-described information providing device 10 may be realized by a plurality of server computers, and depending on the function, the configuration can be flexibly changed, such as by calling an external platform or the like using an API (Application Programming Interface) or network computing.

[0096] In addition, the above-described embodiments and modification examples can be appropriately combined as long as the processing contents do not conflict.

[0097] In addition, the "section (section, module, unit)" described above can be read as "means", "circuit", etc. For example, the control section can be read as a control means or a control circuit.

Explanation of Signs

[0098] 10 Information providing device 11 Communication section 12 Storage section 12a User information storage section 12b Transaction target information storage section 12c Score information storage section 12d Provided information storage section 13 Control section 13a Collection section 13b Calculation section 13c Generation section 13d Transmission section 13e Learning section 20 User terminal 30 Transaction target information database 100 Information providing system

Claims

1. A collection unit that collects user information including at least one of the user's search history and purchase history and transaction target information including the attributes of the transaction target; When the user information and the transaction target information are input, a machine learning model learned to output an ownership score indicating the possibility that the user owns and a purchase tendency score indicating the possibility that the user purchases is used to calculate, for each transaction target, the ownership score and the purchase tendency score; A generation unit that generates provision information to be provided to the user according to the combination of the high and low of the ownership score and the purchase tendency score; An information providing apparatus, characterized by comprising the above.

2. The calculation unit further uses, as the user information, user attributes indicating the user's interests and concerns to calculate, for each transaction target, the ownership score and the purchase tendency score. The information providing apparatus according to claim 1, characterized by the above.

3. When the ownership score of the transaction target is less than a predetermined threshold and the purchase tendency score is equal to or greater than the predetermined threshold, the generation unit generates the provision information for recommending the purchase of the transaction target. The information providing apparatus according to claim 1 or 2, characterized by the above.

4. When the ownership score of the transaction target is equal to or greater than a predetermined threshold and the purchase tendency score is equal to or greater than the predetermined threshold, the generation unit generates the provision information for recommending the purchase of a transaction target related to the transaction target. The information providing apparatus according to claim 1 or 2, characterized by the above.

5. When the ownership score of the transaction target is less than a predetermined threshold and the purchase tendency score is less than the predetermined threshold, the generation unit generates the provision information for the purpose of increasing the purchase tendency score of the transaction target. The information providing apparatus according to claim 1 or 2, characterized by the above.

6. When the ownership score of the transaction target is equal to or greater than a predetermined threshold and the purchase tendency score is less than the predetermined threshold, the generation unit generates the provision information for recommending the purchase of the transaction target to be provided at the time of replacement purchase of the transaction target. The information providing apparatus according to claim 1 or 2, characterized by the above.

7. An information providing method executed by an information providing apparatus, A collection step of collecting user information including at least one of the user's search history and purchase history and transaction target information including the attributes of the transaction target; A calculation step of calculating, for each transaction target, the ownership score indicating the possibility that the user owns and the purchase tendency score indicating the possibility that the user purchases, using a machine learning model trained to output the ownership score and the purchase tendency score when the user information and the transaction target information are input; A generation step of generating provision information to be provided to the user according to the combination of the high and low of the ownership score and the purchase tendency score; An information provision method characterized by including the above.

8. A collection procedure of collecting user information including at least one of the user's search history and purchase history and transaction target information including the attributes of the transaction target; A calculation procedure of calculating, for each transaction target, the ownership score indicating the possibility that the user owns and the purchase tendency score indicating the possibility that the user purchases, using a machine learning model trained to output the ownership score and the purchase tendency score when the user information and the transaction target information are input; A generation procedure of generating provision information to be provided to the user according to the combination of the high and low of the ownership score and the purchase tendency score; An information provision program characterized by causing a computer to execute the above.

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