JP2023125410A - Recommendation device, recommendation system, recommendation method, and program - Google Patents

Abstract

To provide a recommendation device, a recommendation system, a recommendation method, and a program for periodically recommending the purchase of a product with a small greenhouse gas emission factor.SOLUTION: A recommendation device 1 includes: a database 11 in which an attribute of a product is associated with an emission factor indicating a greenhouse gas emission amount per unit price of the product; a calculation section 12 that calculates the greenhouse gas emission amount by a product purchased by a user on the basis of an input from a user device carried by the user and a database 11; and a recommendation section 13 that periodically generates recommendation information for recommending a product whose attribute matches that of a product purchased by the user within a predetermined period and of which the emission factor is smaller than that of the product purchased within the predetermined period.SELECTED DRAWING: Figure 1

Description

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

Environmental household account books that calculate and visualize greenhouse gas (e.g., carbon dioxide) emissions from energy usage such as electricity, gas, or water are attracting attention. Patent Document 1 describes an environmental household account book system in which sales data for each consumer is managed by a business operator and greenhouse gas emissions by each consumer are calculated.

Patent Document 2 describes an environmental load reduction system that compares the superiority of a product in use and a product that is a candidate for purchase in terms of greenhouse gas emissions, and determines whether it is appropriate to replace the product.

Japanese Patent Application Publication No. 2010-282279 JP2011-81511A

In the environmental household account book system described in Patent Document 1, it is necessary for the user to consider means for reducing greenhouse gas emissions. Therefore, there is a problem in that it is difficult to change the user's behavior so as to reduce greenhouse gas emissions.

In view of the above-mentioned problems, an object of the present disclosure is to provide a recommendation device, a recommendation system, a recommendation method, and a program that can regularly recommend the purchase of products with a small greenhouse gas emission coefficient.

The recommended device according to the present disclosure is
a database in which attributes of products are associated with emission coefficients indicating greenhouse gas emissions per unit price of the product;
a calculation unit that calculates greenhouse gas emissions from products purchased by the user based on input from a user device owned by the user and the database;
a recommendation unit that periodically generates recommendation information that recommends a product that has the same attributes as a product purchased by the user within a predetermined period and has a smaller emission coefficient than the product purchased within the predetermined period; Be prepared.

The recommendation system according to this disclosure is
A user device owned by the user;
a recommendation device including a database in which attributes of a product are associated with an emission coefficient indicating greenhouse gas emissions per unit price of the product;
The recommended device is
calculating greenhouse gas emissions from products purchased by the user based on input from the user device and the database;
Recommendation information that recommends a product whose attributes match those of a product purchased by the user within a predetermined period and whose emission coefficient is smaller than the product purchased within the predetermined period is periodically generated.

The recommended method for this disclosure is:
The computer is
Based on input from a user device owned by the user and a database in which attributes of the product are associated with an emission coefficient indicating the amount of greenhouse gas emissions per unit price of the product, a greenhouse by the product purchased by the user is generated. Calculate the amount of effective gas emissions,
Recommendation information that recommends a product whose attributes match those of a product purchased by the user within a predetermined period and whose emission coefficient is smaller than the product purchased within the predetermined period is periodically generated.

The program related to this disclosure is
to the computer,
Based on input from a user device owned by the user and a database in which attributes of the product are associated with an emission coefficient indicating the amount of greenhouse gas emissions per unit price of the product, a greenhouse by the product purchased by the user is generated. Processing to calculate the amount of effective gas emissions,
A process of periodically generating recommendation information that recommends a product whose attributes match a product purchased by the user within a predetermined period and whose emission coefficient is smaller than the product purchased within the predetermined period;
Execute.

The recommendation device, recommendation system, recommendation method, and program according to the present disclosure can regularly recommend the purchase of products with a small greenhouse gas emission coefficient.

1 is a block diagram showing the configuration of a recommendation device according to a first embodiment; FIG. FIG. 2 is a block diagram showing the configuration of a recommendation system according to a second embodiment. FIG. 7 is an explanatory diagram of an example of an emission factor database according to the second embodiment.

Embodiment 1
Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a block diagram showing the configuration of a recommendation device 1 according to the first embodiment. The recommendation device 1 includes a database 11, a calculation section 12, and a recommendation section 13.

The recommended device 1 is connected to a network (not shown). The network may be wired or wireless. A user device (not shown) is connected to the network.

In the database 11, product attributes are associated with emission coefficients indicating greenhouse gas emissions per unit price of the product. The products include, for example, foodstuffs, daily necessities, gas, electricity, and the like. Products may also include services such as cleaning. Attributes include item name, product name, payment destination (purchase location), and the like. The greenhouse gas is, for example, carbon dioxide. Greenhouse gases may include methane, nitrous oxide, and the like.

The calculation unit 12 calculates greenhouse gas emissions from products purchased by the user based on input from a user device owned by the user and the database 11 . A user device is owned by a user. The user device is, for example, a smartphone or a PC (Personal Computer). Input from the user device may include product name, price, and the like. The calculation unit 12 can calculate greenhouse gas emissions by extracting the emission coefficient of the purchased product from the database 11 and multiplying the extracted emission coefficient by the price.

The recommendation unit 13 periodically provides recommendation information that recommends products that match the attributes (for example, item name) of products purchased by the user within a predetermined period and have a lower emission factor than the products purchased within the predetermined period. to be generated. It is assumed that the recommendation device 1 manages the user's purchase history over a predetermined period (for example, one week). The recommendation unit 13 may, for example, refer to the database 11 to determine a recommended product, and send an email containing a message urging purchase of the determined product to the user device.

The recommendation device 1 regularly recommends the purchase of products with small emission factors. Recommended device 1 can contribute to reducing greenhouse gas emissions by encouraging purchase of products with low emission coefficients.

Note that the recommendation device 1 includes a processor, a memory, and a storage device as components not shown. Further, the storage device stores a computer program in which the process of the recommended method according to the first embodiment is implemented. Then, the processor loads a computer program from the storage device into the memory and executes the computer program. Thereby, the processor realizes the functions of the calculation section 12 and the recommendation section 13.

Alternatively, the calculating section 12 and the recommending section 13 may each be realized by dedicated hardware. Further, a part or all of each component of each device may be realized by a general-purpose or dedicated circuit, a processor, etc., or a combination thereof. These may be configured by a single chip or multiple chips connected via a bus. A part or all of each component of each device may be realized by a combination of the circuits and the like described above and a program. Further, as the processor, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an FPGA (field-programmable gate array), a quantum processor (quantum computer control chip), etc. can be used.

Further, in the case where a part or all of each component of the recommended device 1 is realized by a plurality of information processing devices, circuits, etc., the plurality of information processing devices, circuits, etc. may be centrally arranged or distributed. may be placed. For example, information processing devices, circuits, etc. may be realized as a client server system, a cloud computing system, or the like, in which each is connected via a communication network. Further, the functions of the recommended device 1 may be provided in a SaaS (Software as a Service) format.

Embodiment 2
FIG. 2 is a block diagram showing the configuration of a recommendation system 1000 according to the second embodiment. The recommendation system 1000 includes a user device 100 and an emissions calculation server 200. The emission calculation server 200 is a specific example of the recommendation device 1 described above. The user device 100 and the emissions calculation server 200 are connected to enable data communication.

The user device 100 is a terminal owned by a user, such as a smartphone or a PC (Personal Computer). The user device 100 includes an input device (for example, a touch panel, a keyboard, or a mouse) and a display device (for example, a display or a monitor), which are not shown.

The user device 100 transmits the expenditure information input by the user to the emissions calculation server 200. The expenditure information includes the product name, price, and place of purchase of the purchased product. The user may launch the software for creating an environmental household account book and input expenditure information. Note that the user may use the user device 100 to register address information (for example, prefecture name, city, town, village name), email address, etc. in the emissions calculation server 200 in advance.

The user device 100 also acquires information from the emissions calculation server 200 and causes the display device to display the environmental household account book. The user device 100 may acquire information from an emissions calculation database 230, which will be described later.

Further, the user device 100 acquires recommended information from the emissions calculation server 200 and causes the display device to display the recommended information. The recommended information includes the product name of the recommended product, information indicating the purchase location, and the like.

The emission calculation server 200 is a specific example of the recommendation device 1 described above. The emission amount calculation server 200 includes an emission factor database 210, an emission amount calculation section 220, an emission amount calculated database 230, a ranking creation section 240, and a recommended information creation section 250.

The emission factor database 210 is a specific example of the database 11 described above. The emission factor database 210 stores attributes such as product names and emission factors in association with each other.

FIG. 3 shows a specific example of the emission factor database 210. The emission factor database 210 includes a field 211 indicating the payment destination (purchased place), a field 212 indicating the manufacturer, a field 213 indicating the product name, a field 214 indicating the emission factor, a field 215 indicating the validity period, and a field 215 indicating the region. It includes a field 216 indicating the name. Field 211, field 212, field 213, and field 216 represent attributes of the product.

Field 211 indicates the payee of the purchased product, that is, the purchaser. Field 212 indicates the producer or manufacturer of the purchased item. Field 213 indicates the product name of the purchased product. Field 214 indicates the carbon dioxide emissions per unit price of the purchased item. Field 215 indicates the validity period of the emission factor shown in field 214. Field 216 indicates the sales area where the purchased item is sold. The emission calculation server 200 receives the latest emission coefficients from each business operator and updates the emission coefficient database 210.

Each record of the emission factor database 210 stores information regarding one product. For example, in the example emission factor record 210a of the emission factor database 210 shown in FIG. 3, the emission factor of beef produced by Company A and sold at T Store is 0.005 (unit: kg/yen). It is shown that. Further, the emission factor record 210a indicates that the effective period of the emission factor is from July 1, 2021 to July 31, 2021, and that beef is sold in Yokohama City.

Returning to FIG. 2, the explanation will be continued. The emission amount calculation unit 220 is a specific example of the calculation unit 12 described above. Emission calculation unit 220 acquires expenditure information including product name and price from user device 100. Next, the emission calculation unit 220 extracts the emission coefficient associated with the product name from the emission coefficient database 210. Next, the emission calculation unit 220 calculates the emission amount by multiplying the extracted emission coefficient by the obtained price. The emissions calculation unit 220 registers the calculated emissions in the emissions calculated database 230 in association with the product name and user ID.

Further, the emission amount calculation unit 220 outputs the calculated emission amount to the user device 100. This allows the user to check the carbon dioxide emissions of the purchased product.

In the emissions calculated database 230, carbon dioxide emissions are recorded for each product purchased by the user. Furthermore, the emission amount calculated database 230 records the purchase date of each product. The user device 100 may refer to the emissions calculated database 230 to generate an environmental household account book. The environmental household account book includes carbon dioxide emissions from each of a plurality of products purchased by the user within a collection period (for example, one month). The environmental household account book may include the sum of carbon dioxide emissions of each of a plurality of products.

The ranking creation unit 240 calculates the sum of carbon dioxide emissions of each of a plurality of products purchased within a collection period (for example, one week or one month). The ranking creation unit 240 creates a ranking of a plurality of users so that the user with the smaller sum of carbon dioxide emissions ranks higher, and notifies each user of the ranking. Thereby, the recommendation system 1000 can increase each user's motivation to reduce carbon dioxide emissions.

The ranking creation unit 240 may group a plurality of users based on household composition, region, age, gender, etc., and create rankings for each group. Since carbon dioxide emissions may vary depending on household composition, etc., by appropriately grouping users, each user can be further motivated to reduce carbon dioxide emissions. It is assumed that the emissions calculation server 200 stores user information including each user's ID, region, age, and the like. In the emissions calculation server 200, user information and an environmental household account book may be associated with each other.

The ranking creation unit 240 may send the rankings by email. The transmitted content also includes information on the user's rank among users. For example, the ranking creation unit 240 sends a message saying, "Among the households with the same household composition in the municipality where you live, your household ranks 0 out of 0."

The recommendation information creation unit 250 is a specific example of the recommendation unit 13 described above. The recommended information creation unit 250 selects products whose attributes (e.g., product name) match those of products purchased by the user within a predetermined period (e.g., one week) and whose emission coefficient is smaller than the products purchased within the predetermined period. Recommendation information is generated periodically (for example, every week). Recommended information creation section 250 outputs the generated recommended information to user device 100.

Referring to FIG. 3, Company C's milk has a smaller emission factor than Company B's milk. Also, both Company C's milk and Company B's milk are sold in Yokohama City. In such a case, the recommendation information creation unit 250 recommends that the user who has purchased milk from Company B sold at T Store purchase milk from Company C sold at I Store. The recommendation information creation unit 250 generates a message such as "Milk from Company C sold at the I store is recommended because it has a small carbon dioxide emission coefficient" and transmits it to the user device 100. When the recommended information includes information indicating a purchase location (for example, I-store), it is possible to reduce the time and effort required for the user to research the purchase location. For example, the recommendation information creation unit 250 may recommend products about once a week.

Specifically, the recommended information creation unit 250 first identifies products purchased by each user within a predetermined period from the discharge amount calculated database 230. Next, the recommended information creation unit 250 extracts from the emission factor database 210 products that have the same product name and sales region as the specified product and have a lower emission factor than the specified product. Then, the recommendation information generation unit 250 generates recommendation information that recommends the extracted products.

The recommendation information may recommend one product or may recommend a plurality of products. Recommended products may be determined according to the number of purchases. Further, the recommendation information creation unit 250 may recommend products in order of effectiveness in reducing carbon dioxide emissions (for example, products with large carbon dioxide emissions).

The recommendation information creation unit 250 may make recommendations based on the ranking described above. The recommended information creation unit 250 may transmit recommended information only to users who are ranked lower in the ranking. This is because users who are ranked lower are considered to particularly need recommended information.

The emissions calculation server 200 may include a counting unit (not shown) that counts the number of times each product is recommended. Since the number of sales of each product is thought to increase according to the number of times it is recommended, product producers and sellers may want to know the number of times they have been recommended.

Finally, the effects of the recommendation system 1000 will be explained. Although it is not an actual household account book, there is something called an environmental household account book. When creating an environmental household account book, users can calculate their household's carbon dioxide emissions from energy usage charges such as electricity, gas, and water. Such technology makes it possible to present carbon dioxide emissions to the user. However, users had to think about how to reduce carbon dioxide emissions by themselves based on the displayed numbers. Another problem was that changes in user behavior did not last long because the benefits were difficult to understand.

The recommendation system according to the second embodiment notifies each user of recommendation information and ranking. Thereby, each user can be motivated to reduce the amount of carbon dioxide emitted in daily life. In addition, by purchasing the recommended product with a small emission coefficient, the effect is that it leads to a reduction in carbon dioxide emissions. Furthermore, by linking multiple users with businesses that provide products with low carbon dioxide emissions, it is possible to promote reductions in carbon dioxide emissions.

Note that the present invention is not limited to the above embodiments, and can be modified as appropriate without departing from the spirit.

1 Recommendation device 11 Database 12 Calculation unit 13 Recommendation unit 1000 Recommendation system 100 User device 200 Emission calculation server 210 Emission factor database 211, 212, 213, 214, 215, 216 Field 220 Emission calculation unit 230 Emission calculation database 240 Ranking Creation Department 250 Recommended Information Creation Department

Claims (9)

a database in which attributes of a product are associated with an emission coefficient indicating greenhouse gas emissions per unit price of the product;
a calculation unit that calculates greenhouse gas emissions from products purchased by the user based on input from a user device owned by the user and the database;
a recommendation unit that periodically generates recommendation information that recommends a product that has the same attribute as a product purchased by the user within a predetermined period and has a smaller emission coefficient than the product purchased within the predetermined period; Recommended equipment to have. The recommended device is
a ranking creation unit that creates a ranking of a plurality of users so that the user with a lower sum of greenhouse gas emissions of each of the plurality of products purchased within the aggregation period is ranked higher, and notifies each user of the ranking;
The recommendation device according to claim 1, comprising: The ranking creation department is
grouping multiple users and creating the ranking for each group;
The recommendation device according to claim 2. The recommended information includes information indicating where to purchase the recommended product.
A recommendation device according to any one of claims 1 to 3. A counting section that counts the number of times each product is recommended for purchase;
The recommendation device according to any one of claims 1 to 4, further comprising:. A user device owned by the user;
a recommendation device including a database in which attributes of a product are associated with an emission coefficient indicating greenhouse gas emissions per unit price of the product;
The recommended device is
calculating greenhouse gas emissions from products purchased by the user based on input from the user device and the database;
regularly generating recommendation information that recommends a product that has the same attributes as a product purchased by the user within a predetermined period and has a smaller emission coefficient than the product purchased within the predetermined period;
Recommended system. The recommended device is
Creating a ranking of a plurality of users so that the user with a lower sum of greenhouse gas emissions of each of the plurality of products purchased within the aggregation period is ranked higher, and notifying each user of the ranking,
The recommendation system according to claim 6. The computer is
Based on input from a user device owned by the user and a database in which attributes of the product are associated with an emission coefficient indicating the amount of greenhouse gas emissions per unit price of the product, a greenhouse by the product purchased by the user is generated. Calculate the amount of effective gas emissions,
regularly recommending products whose attributes match those of products purchased by the user within a predetermined period and whose emission coefficient is smaller than the products purchased within the predetermined period;
Recommended method. to the computer,
Based on input from a user device owned by the user and a database in which attributes of the product are associated with an emission coefficient indicating the amount of greenhouse gas emissions per unit price of the product, a greenhouse by the product purchased by the user is generated. Processing to calculate the amount of effective gas emissions,
A process of periodically generating recommendation information that recommends a product whose attributes match a product purchased by the user within a predetermined period and whose emission coefficient is smaller than the product purchased within the predetermined period;
A program to run.

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