JP7544253B2 - How to decide product placement - Google Patents

Description

The present invention relates to a product placement determination method, a product placement determination device, and a program.

In retail stores such as convenience stores and supermarkets, products are mainly displayed and sold on shelves installed within the store. Since the arrangement of products on the shelves affects the convenience for customers when purchasing products and product sales, product arrangements are frequently changed. For example, as described in Patent Literature 1, product arrangements are changed based on product arrangements and sales forecasts. Specifically, in Patent Literature 1, product arrangement candidates on the shelves are generated, sales are forecast for each of the generated product arrangement candidates, and product arrangements are determined based on the sales forecasts.

International Publication No. 2016/199405

However, with the above-mentioned method, information on past product sales performance is required to predict sales for each product placement candidate. This creates the problem that it takes time and effort to determine the placement of products on product shelves. This problem is not limited to cases where products are placed on product shelves for sale, but can also occur when products are placed in a store area or when product displays are placed in a screen area on a website for sale.

Therefore, the object of the present invention is to provide a product placement determination method that can solve the above-mentioned problem of the time and effort required when determining the placement of products to be sold.

A product placement determination method according to one embodiment of the present invention includes the steps of:
generating relationship information representing a relationship between a position in a placement area and the product based on product placement information representing a placement status of the product in a placement area in which the product to be sold is placed and product information relating to the product;
determining a position of the product in the placement area based on the relationship information;
The structure is as follows.

In addition, a product placement determination device according to one embodiment of the present invention includes:
a relationship generating unit that generates relationship information that represents a relationship between a position in a placement area and a product based on product placement information that represents a placement status of the product in the placement area where the product is placed and product information related to the product;
a placement determination unit that determines a position of a product in the placement area based on the relationship information;
Equipped with
The structure is as follows.

In addition, a program according to one aspect of the present invention includes:
In the information processing device,
generating relationship information representing a relationship between a position in a placement area and the product based on product placement information representing a placement status of the product in a placement area in which the product to be sold is placed and product information relating to the product;
determining a position of the product in the placement area based on the relationship information;
Execute the process,
The structure is as follows.

By being configured as described above, the present invention makes it possible to determine the placement of products to be sold with reduced effort and time.

1 is a block diagram showing a configuration of a product placement determination device according to a first embodiment of the present invention. FIG. 2 is a diagram showing an example of product placement information used by the product placement determination device disclosed in FIG. 1 . 2 is a diagram showing an example of information representing a relationship between a product and a position generated by the product placement determination device disclosed in FIG. 1 . 2 is a diagram showing an example of information representing a relationship between a product and a position generated by the product placement determination device disclosed in FIG. 1 . 2 is a diagram showing an example of information representing a relationship between a product and a position generated by the product placement determination device disclosed in FIG. 1 . 2 is a diagram showing an example of product positions determined by the product placement determination device disclosed in FIG. 1 . FIG. 2 is a diagram showing an example of product positions determined by the product placement determination device disclosed in FIG. 1 . FIG. 10 is a diagram showing an example of a display of information representing a relationship between a product and a position generated by the product placement determination device disclosed in FIG. 1 . 4 is a flowchart showing the operation of the product placement determination device disclosed in FIG. 1 . FIG. 11 is a block diagram showing a hardware configuration of a product placement determination device according to a second embodiment of the present invention. FIG. 11 is a block diagram showing a configuration of a product placement determination device according to a second embodiment of the present invention. 10 is a flowchart showing the operation of the product placement determination device according to the second embodiment of the present invention.

<Embodiment 1>
A first embodiment of the present invention will be described with reference to Figures 1 to 9. Figure 1 is a diagram for explaining the configuration of a product placement determination device, and Figures 2 to 9 are diagrams for explaining the processing operation of the product placement determination device.

[composition]
The product placement determination device 10 of the present invention is used to determine the placement of products on product shelves installed in a retail store such as a convenience store or supermarket when selling products in the store. However, the product placement determined by the present invention is not necessarily limited to the placement on product shelves in the store, but may be the placement of products in the entire store area, the placement of products in a screen area when selling products on a website, or the placement of products in any sales area. Furthermore, the product in the present invention may be a specific brand of product itself, or a group of products classified into categories such as manufacturers and product types.

The product placement determination device 10 is composed of one or more information processing devices each including a calculation device and a storage device. As shown in FIG. 1, the product placement determination device 10 is connected to an input device 20 and a display device 30. The product placement determination device 10 also includes a learning unit 11, a calculation unit 12, a determination unit 13, and an output unit 14. The functions of the learning unit 11, the calculation unit 12, the determination unit 13, and the output unit 14 can be realized by the calculation device executing a program for realizing each function stored in the storage device. The product placement determination device 10 also includes a placement information storage unit 16, a weight information storage unit 17, and a score storage unit 18. The placement information storage unit 16, the weight information storage unit 17, and the score storage unit 18 are each composed of a storage device. Each component will be described in detail below.

First, the placement information storage unit 16 stores product placement information that represents the placement status of products on a product shelf. The product placement information is, for example, information that represents the placement of products on a product shelf that was actually determined in the past by an expert who performs the task of determining the placement of products. As an example, the product placement information is configured by associating product information that identifies products such as "AAA" and "BBB" that are placed at such positions with position information that represents the positions of the product shelf, such as "1A" and "1B," as shown in FIG. 2. In this way, the product placement information is configured with information on the position of the product shelf and the products placed at such positions, and therefore it can be said to represent the placement status of products that was previously set by an expert, such as the position of the product on the product shelf, the positional relationship between the products, and the placement area of the products. Note that the product information includes, as information representing the attributes of the product, the product name, the manufacturer name, the type (category (coffee, water, tea, carbonated drinks, etc.)), the size (350 ml, 500 ml, 2 L), the price, the production date (whether the product is new, etc.), etc. Note that the product information is not limited to the above as long as it is information regarding the attributes of the product. In addition, the product placement information may include not only position information and product information, but also sales status information that represents the sales status due to such placement. For example, the information may include sales of each product, the age group and sex of the purchaser of each product, the purchaser's stay time in the store, the location of the store, etc. The product placement information is input to the product placement determination device 10 by an operator from the input device 20, and is stored in the placement information storage unit 16.

Based on the above-mentioned product placement information, the learning unit 11 (relationship generation unit) generates relationship information that represents the relationship between the position on the product shelf and the product to be placed. For example, the learning unit 11 performs inverse reinforcement learning (or intention learning) on multiple product placement information to generate relationship information on the content of what product and what placement the expert placed in the past. That is, the learning unit 11 performs inverse reinforcement learning on multiple product placement information related to the product placement of past experts, thereby generating relationship information that reflects the knack (or tacit knowledge, skill) of the past expert in product placement. As an example, the learning unit 11 generates relationship information including weight information that represents the weight for each position on the product shelf for each product. At this time, the learning unit 11 generates weight information such that the more frequently a product is placed in the same position, the higher the weight for that position of the product. In this way, the learning unit 11 generates weights for each position on the product shelf for each product, for example, as shown in FIG. 3. In the example of FIG. 3, the weights are calculated as values between 0 and 1.

For example, the learning unit 11 generates relationship information reflecting the characteristics of products, purchasers, and stores by performing inverse reinforcement learning (or intention learning) on sales status information related to at least one of the following items included in the product placement information: sales for each product, age group and sex of the purchaser for each product, the purchaser's stay time in the store, and the location of the store. In this case, for example, the learning unit 11 may generate relationship information such that the weight of the placement position increases the higher the sales of the product, or the weight of the placement position increases the higher the frequency of purchase by a specific age group of purchasers. For example, the learning unit 11 may generate relationship information such that the weight of the placement position increases for products that have high sales despite a short stay time of the purchaser, or products that have high sales despite a poor store location.

Based on the above-mentioned product placement information, the learning unit 11 generates information including weight information representing the weight of each product relative to the positional relationship with other products on the product shelf as relationship information representing the relationship between the position on the product shelf and the product. More specifically, the learning unit 11 generates weight information such that the more frequently a product is positioned adjacent to another specific product, the higher the weight between the products. In this way, the learning unit 11 generates weights for each product relative to each other product, for example, as shown in FIG. 4. In the example of FIG. 4, the weights are calculated as values between 0 and 1. Here, the learning unit 11 may calculate the weight of each product relative to each other product by taking into account sales status information included in the product placement information, such as sales of each product, age group and sex of each purchaser of the product, the purchaser's stay time in the store, and the location of the store. For example, the learning unit 11 may increase the weight of other products placed adjacent to the product the higher the sales of the product.

Based on the above-mentioned product placement information, the learning unit 11 generates information including weight information representing the weight of the placement area of each product on the product shelf as relationship information representing the relationship between the position on the product shelf and the product. More specifically, the learning unit 11 generates weights for the so-called number of faces representing the number of times a product is placed at the forefront of the product shelf. As an example, the learning unit 11 generates weight information such that the weight for the number of faces increases as the frequency of the number of faces on which the product is placed increases. In this way, the learning unit 11 generates weights for each number of faces for each product, for example, as shown in FIG. 5. In the example of FIG. 5, the weights are calculated as values between 0 and 1, and the number of faces is 5 or less. Here, the learning unit 11 may calculate the weight for the number of faces for each product by taking into account sales status information such as sales of each product, age group and sex of each purchaser of the product, the purchaser's stay time in the store, and the location of the store, which are included in the product placement information. For example, the learning unit 11 may increase the weight for the number of faces as the sales of the product increase.

Then, the learning unit 11 stores the generated relevance information, which is each piece of weight information, in the weight information storage unit 17. Note that it is preferable that the learning unit 11 performs learning based on a large number of pieces of product placement information and generates the relationship information consisting of the above-mentioned weight information, but it may also perform learning using at least one piece of product placement information.

The calculation unit 12 (evaluation unit) generates evaluation information for evaluating the arrangement of products on the product shelf based on the relationship information. Specifically, the calculation unit 12 (evaluation unit) uses the weight information described above to calculate a score, which is evaluation information for evaluating the arrangement of products on the product shelf. For example, the calculation unit 12 can use the following formula 1 to calculate a score for the arrangement of products on the product shelf corresponding to the specified product arrangement information as shown in FIG. 2. Note that the product arrangement information for which the score is calculated is not the content representing the arrangement of products set by the expert who is the subject of the above-mentioned learning, but represents, for example, the arrangement of products on the product shelf in the current store, or the arrangement of products on the product shelf newly thought up by the store staff. The product arrangement information for which the score is calculated is input by the operator from the input device 20.

Here, in the formula 1, _xij is a value indicating whether or not the product i is located at the product shelf position j, and if so, 1 is set, and if not, 0 is set. In addition, in the formula 1, _yij is a value indicating whether or not the product i is adjacent to another product j, and if so, 1 is set, and if not, 0 is set. In addition, in the formula 1, _zi is a value indicating the number of faces of the product i, and the value of the number of faces is set as is. The values of x, y, and z are calculated by the calculation unit 12 based on the product placement information as shown in FIG. 2. However, the values of x, y, and z may be extracted in advance by an operator from the product placement information and input from the input device 20.

In addition, in the formula 1, α _ij is set as weight information representing the weight for each position j on the product shelf for each product i as shown in FIG. 3. Therefore, the value of α _ij x _ij in the formula 1 is calculated to be high when the product is located at a product shelf position where the weight value is set high in the product placement information to be evaluated. In addition, in the formula 1, β _ij is set as weight information representing the weight for each other product j adjacent to the product i on the product shelf as shown in FIG. 4. Therefore, the value of β _ij y _ij in the formula 1 is calculated to be high when each product adjacent to the product is located in the product placement information to be evaluated where the product is located in the product placement information where the weight value is set high. In addition, in the formula 1, γ _i is set as weight information representing the weight for the number of faces on the product shelf for each product i as shown in FIG. 5. Therefore, the value of γ _i z _i in the formula 1 is calculated to be high when the product is located with the number of faces where the weight value is set high in the product placement information to be evaluated.

Furthermore, the calculation unit 12 calculates a score corresponding to the placement status of each product in the multiple product placement information. At this time, the calculation unit 12 may calculate the scores for each of the multiple product placement information input by the operator, or may calculate the scores for other product placement information obtained by changing the content of one product placement information input by the operator. For example, the calculation unit 12 repeats multiple times the process of generating other product placement information by randomly changing the placement of the products from the placement status of the products in one input product placement information or according to a preset rule, and calculates the score for each of the other product placement information. Then, the calculation unit 12 stores the calculated scores in the score storage unit 18 together with the product placement information.

The determination unit 13 (placement determination unit) determines the placement of products on a product shelf represented by specific product placement information based on the score value corresponding to each product placement information calculated using the weight information as described above. For example, the determination unit 13 determines the placement of the product for the product placement information with the highest score among the scores corresponding to each product placement information. Note that, if the calculation unit 12 calculates the score each time it changes the product placement information and generates new product placement information, the determination unit 13 may determine the placement of the product corresponding to the product placement information at that time when a score higher than the score for the initial product placement information is calculated.

The output unit 14 (placement determination unit, relationship generation unit) outputs the placement of products on the product shelf determined as described above to be displayed on the display device 30. For example, as shown in FIG. 6, the output unit 14 associates product information specifying products such as "AAA" and "BBB" that have been determined to be placed in the product shelf with position information indicating the product shelf position such as "1A" and "1B", and displays the associated information. The output unit 14 may output the placement of products on the determined product shelf to be displayed in any display method. For example, as shown in FIG. 7, the output unit 14 displays a table consisting of the positions of each product and each product shelf, and displays 1 in the places where the product determined to be placed corresponds to the product shelf position, and displays 0 elsewhere.

The output unit 14 may also output relevance information including the weight information generated by the learning unit 11 as described above, for example, weight information as shown in each of Figures 3 to 5, for display on the display device 30. At this time, the output unit 14 may display the weight information as shown in Figures 3 to 5 as is, or may change the display form of the generated weight information and display it. For example, the output unit 14 may rearrange and display the products in descending order of weight for each product shelf position, as shown in Figure 8, based on the weight value for each product for each product shelf position as shown in Figure 5. This causes the products to be displayed in the order that is considered desirable for preferential placement at each product shelf position.

[Action]
Next, the operation of the above-mentioned product placement determination device 10 will be described mainly with reference to the flowchart in Fig. 9. First, the product placement determination device 10 learns past product placement information stored in the placement information storage unit 16, and generates weight information for the placement status of products on a product shelf (step S1). For example, the product placement determination device 10 generates weight information for each position on the product shelf for each product shown in Fig. 3, weight information for each other product for each product shown in Fig. 4, and weight information for each number of faces for each product shown in Fig. 5.

Next, the product placement determination device 10 receives input of product placement information representing the current product placement status of the store, and calculates the score of the product placement information (first evaluation information) (step S2). At this time, the product placement determination device 10 calculates the score by the above formula 1 using the weight information generated as described above. Next, the product placement determination device 10 further generates new product placement information in which the product placement status in the current product placement information is changed, and calculates the score of the new product placement information (second evaluation information) (step S3). Then, the product placement determination device 10 compares the score of the current product placement information with the score of the new product placement information after the change (step S4). If the result of the comparison shows that the score of the new product placement information after the change does not exceed the score of the current product placement information (No in step S4), the product placement determination device 10 further generates new product placement information in which the product placement status in the current product placement information is changed, calculates the score of the new product placement information (second evaluation information) (step S3), and compares it with the score of the current product placement information (step S4).

The product placement determination device 10 repeats changing the product placement information and calculating the score (second evaluation information) as described above (step S3) until the score of the new product placement information after the change exceeds the score of the current product placement information (No in step S4). Then, when the score of the new product placement information after the change exceeds the score of the current product placement information (Yes in step S4), the product placement determination device 10 determines the placement of the products on the product shelf represented by the new product placement information after the change (step S5). The product placement determination device 10 outputs the determined product placement on the product shelf to the display device 30 for display. Note that the product placement determination device 10 may output relevance information including the weight information generated as described above to the display device 30 at any timing for display.

In addition, the product placement determination device 10 may generate multiple new product placement information by changing the current product placement information, and then calculate the scores of all the new product placement information.The product placement determination device 10 may then determine the placement of the product based on the new product placement information that has the highest score among the scores of the new product placement information that is higher than the score of the current product placement information.

As described above, according to the present invention, relationships between products and positions are generated from past product placement information, and new product placements are determined based on these relationships. This makes it possible to determine the placement of products to be sold with minimal effort and time.

[Modification]
Here, in the above, an example has been given of the product placement determination device 10 being used to determine the placement of products on product shelves installed in a store, but as will be described below, it may also be used to determine the placement of products in the entire store area, or the placement of products in a screen area when selling products on a website.

As an example, when determining the placement of products in the entire store area in a warehouse-type store, the location information included in the above-mentioned product placement information is information that identifies the location in the entire store area. For example, the location information may be an address or area ID assigned to each section in which products are placed within the store. In this case, the location information is not limited to the above-mentioned address or area ID, as long as it is information that can identify the location within the store.
In addition, when deciding the placement of products in a screen area when selling products on a website, the position information included in the above-mentioned product placement information is information that specifies the position in the screen area of the website. For example, the position information may be a number assigned to each display location that displays the product set on the website. In this case, the position information is not limited to the above example, as long as it is information that can specify the position on the website screen.

The product placement determination device 10 then associates product information specifying the products placed at each position with the above-mentioned in-store position information and in-screen area position information, and generates relationship information representing the relationship between the position and the product using the product placement information, as described above. Furthermore, the product placement determination device 10 uses the relationship information, as described above, to calculate a score for the product placement situation at the position in the store or in the screen area, and determines a new product placement based on the score value. In this way, the product placement determination device 10 can determine the placement of products in the entire store area, or the placement of products in the screen area when selling products on a website. Note that the area for which the product placement determination device 10 determines the product placement may be any area.

<Embodiment 2>
Next, a second embodiment of the present invention will be described with reference to Fig. 10 to Fig. 12. Fig. 10 to Fig. 11 are block diagrams showing the configuration of a product placement determination device in the second embodiment, and Fig. 12 is a flowchart showing the operation of the product placement determination device. Note that this embodiment shows an outline of the configuration of the product placement determination device and product placement determination method described in the above embodiments.

First, the hardware configuration of the product placement determination device 100 in this embodiment will be described with reference to Fig. 10. The product placement determination device 100 is configured as a general information processing device, and is equipped with the following hardware configuration, as an example.
・CPU (Central Processing Unit) 101 (arithmetic unit)
・ROM (Read Only Memory) 102 (storage device)
・RAM (Random Access Memory) 103 (storage device)
Program group 104 loaded into RAM 103
A storage device 105 for storing the program group 104
A drive device 106 that reads and writes data from and to a storage medium 110 outside the information processing device.
A communication interface 107 that connects to a communication network 111 outside the information processing device
Input/output interface 108 for inputting and outputting data
A bus 109 that connects each component

The product placement determination device 100 can be equipped with the relationship generation unit 121 and placement determination unit 122 shown in FIG. 11 by having the CPU 101 acquire and execute the program group 104. The program group 104 is stored in the storage device 105 or ROM 102 in advance, for example, and the CPU 101 loads the program group 104 into the RAM 103 and executes it as necessary. The program group 104 may be supplied to the CPU 101 via the communication network 111, or may be stored in the storage medium 110 in advance, and the drive device 106 may read out the program and supply it to the CPU 101. However, the relationship generation unit 121 and placement determination unit 122 described above may be constructed with dedicated electronic circuits for realizing such means.

Note that Figure 10 shows an example of the hardware configuration of an information processing device that is the product placement determination device 100, and the hardware configuration of the information processing device is not limited to the above-mentioned case. For example, the information processing device may be configured with a part of the above-mentioned configuration, such as not having the drive device 106.

Then, the product placement determination device 100 executes the product placement determination method shown in the flowchart of Figure 12 using the functions of the relationship generation unit 121 and the placement determination unit 122 constructed by the program as described above.

As shown in FIG. 12, the product placement determination device 100
Based on product placement information representing the placement status of a product in a placement area in which the product to be sold is placed and product information relating to the product, relationship information representing a relationship between a position in the placement area and the product is generated (step S101);
determining a position of the product in the placement area based on the relationship information (step S102);
The following process is executed.

The present invention, configured as described above, generates a relationship between products and positions from product placement information and product information, and determines the placement of new products based on this relationship. This makes it possible to determine the placement of products to be sold while reducing the effort and time required.

The above-mentioned program can be stored and supplied to a computer using various types of non-transitory computer readable media. Non-transitory computer readable media include various types of tangible storage media. Examples of non-transitory computer readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R/Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)). The program may also be supplied to a computer by various types of transitory computer readable media. Examples of transitory computer readable media include electrical signals, optical signals, and electromagnetic waves. The transitory computer readable media can supply the program to a computer via wired communication paths such as electric wires and optical fibers, or wireless communication paths.

Although the present invention has been described above with reference to the above-mentioned embodiments, the present invention is not limited to the above-mentioned embodiments. Various modifications that can be understood by a person skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. In addition, at least one or more of the functions of the relationship generation unit and the placement determination unit described above may be executed by an information processing device installed and connected on a network, that is, they may be executed by so-called cloud computing.

<Additional Notes>
A part or all of the above-described embodiments can be described as follows: Below, an outline of the configuration of the product placement determination method, the product placement determination device, and the program of the present invention will be described. However, the present invention is not limited to the following configuration.
(Appendix 1)
generating relationship information representing a relationship between a position in a placement area and the product based on product placement information representing a placement status of the product in a placement area in which the product is placed for sale and product information relating to the product;
determining a position of the product in the placement area based on the relationship information;
How product placement is determined.
(Appendix 2)
The product placement determination method according to claim 1,
generating evaluation information for evaluating a specific arrangement state of the products in the arrangement area based on the relationship information;
determining a position of the product in the placement area based on the evaluation information;
How product placement is determined.
(Appendix 3)
The product placement determination method according to claim 2,
generating a plurality of pieces of evaluation information for each of a plurality of product placement situations in the placement area based on the relationship information;
determining a position of the product in the placement area specified by any one of the plurality of placement situations based on the evaluation information;
How product placement is determined.
(Appendix 4)
The product placement determination method according to claim 3,
generating, based on the relationship information, a first piece of evaluation information for one arrangement situation of the product in the arrangement area and a second piece of evaluation information for another arrangement situation different from the one arrangement situation;
determining a position of the product in the placement area specified by a placement status related to evaluation information having a larger value out of the first evaluation information and the second evaluation information;
How product placement is determined.
(Appendix 5)
A product placement determination method according to any one of Supplementary Note 2 to 4,
generating the relationship information including weight information representing a weight for each position in the placement area for each product based on the product placement information and the product information;
generating the evaluation information of a predetermined arrangement state of the products in the arrangement area based on the weight information;
How product placement is determined.
(Appendix 6)
A product placement determination method according to any one of Supplementary Note 2 to 5,
generating the relationship information including weight information representing a weight for a positional relationship between each product and each of the other products in the placement area based on the product placement information and the product information;
generating the evaluation information of a predetermined arrangement state of the products in the arrangement area based on the weight information;
How product placement is determined.
(Appendix 7)
A product placement determination method according to any one of Supplementary Note 2 to 6,
generating the relationship information including weight information representing a weight for a placement area in the placement region for each product based on the product placement information and the product information;
generating the evaluation information of a predetermined arrangement state of the products in the arrangement area based on the weight information;
How product placement is determined.
(Appendix 8)
A product placement determination method according to any one of Supplementary Note 2 to 7,
generating the relationship information including weight information representing a weight corresponding to an appearance frequency of a preset arrangement state in the arrangement area for each product based on the product arrangement information and the product information;
generating the evaluation information of a predetermined arrangement state of the products in the arrangement area based on the weight information;
How product placement is determined.
(Appendix 9)
A product placement determination method according to any one of Supplementary Note 1 to 8,
generating the relationship information reflecting tacit knowledge of the product arrangement methods of past experts by performing inverse reinforcement learning on the plurality of product arrangement information regarding the product arrangements of past experts;
How product placement is determined.
(Appendix 10)
A product placement determination method according to any one of Supplementary Note 1 to 9,
generating the relationship information reflecting at least one characteristic of the product, the purchaser, and the store by performing inverse reinforcement learning on sales status information related to at least one of the following, which is included in the product placement information: sales of each product, the age group and sex of the purchaser of each product, the purchaser's stay time in the store, and the location of the store;
How product placement is determined.
(Appendix 11)
A product placement determination method according to any one of Supplementary Note 1 to 10,
outputting the determined position of the product in the placement area;
How product placement is determined.
(Appendix 12)
A product placement determination method according to any one of Supplementary Note 1 to 11,
outputting information based on the generated relationship information;
How product placement is determined.
(Appendix 13)
a relationship generating unit that generates relationship information that represents a relationship between a position in a placement area and a product based on product placement information that represents a placement status of the product in the placement area where the product is placed and product information related to the product;
a placement determination unit that determines a position of a product in the placement area based on the relationship information;
A product placement determination device comprising:
(Appendix 14)
The product placement determination device according to claim 13,
an evaluation unit that generates evaluation information that evaluates a predetermined arrangement state of the products in the arrangement area based on the relationship information;
The placement determination unit determines a position of the product in the placement area based on the evaluation information.
Product placement determination device.
(Appendix 15)
The product placement determination device according to claim 14,
the evaluation unit generates a plurality of pieces of evaluation information for each of a plurality of product placement situations in the placement area based on the relationship information;
the placement determination unit determines a position of the product in the placement area specified by any one of the plurality of placement situations based on the plurality of pieces of evaluation information;
Product placement determination device.
(Appendix 16)
The product placement determination device according to claim 15,
the evaluation unit generates, based on the relationship information, first evaluation information for one arrangement situation of the product in the arrangement area and second evaluation information for an arrangement situation different from the one arrangement situation;
the placement determination unit determines a position of the product in the placement area specified by a placement situation related to evaluation information having a larger value out of the first evaluation information and the second evaluation information.
Product placement determination device.
(Appendix 17)
A product placement determination device according to any one of appendixes 14 to 16,
the relationship generation unit generates the relationship information including weight information representing a weight for each position in the placement area for each product based on the product placement information and the product information;
the evaluation unit generates the evaluation information of a predetermined arrangement state of the product in the arrangement area based on the weight information.
Product placement determination device.
(Appendix 18)
A product placement determination device according to any one of appendixes 14 to 17,
the relationship generation unit generates the relationship information including weight information representing a weight of a positional relationship between each product and each of the other products in the placement area based on the product placement information and the product information;
the evaluation unit generates the evaluation information of a predetermined arrangement state of the product in the arrangement area based on the weight information.
Product placement determination device.
(Appendix 19)
A product placement determination device according to any one of appendixes 14 to 18,
the relationship generation unit generates the relationship information including weight information representing a weight for a placement area in the placement region for each product based on the product placement information and the product information;
the evaluation unit generates the evaluation information of a predetermined arrangement state of the product in the arrangement area based on the weight information.
Product placement determination device.
(Appendix 20)
A product placement determination device according to any one of appendixes 14 to 19,
the relationship generation unit generates the relationship information including weight information representing a weight corresponding to an appearance frequency of a preset arrangement state in the arrangement area for each product, based on the product arrangement information and the product information;
the evaluation unit generates the evaluation information of a predetermined arrangement state of the product in the arrangement area based on the weight information.
Product placement determination device.
(Appendix 21)
A product placement determination device according to any one of Supplementary Notes 13 to 20,
The relationship generation unit generates the relationship information reflecting tacit knowledge of the product arrangement methods of past experts by performing inverse reinforcement learning on a plurality of pieces of product arrangement information regarding the product arrangements of past experts.
Product placement determination device.
(Appendix 22)
A product placement determination device according to any one of appendixes 13 to 21,
the relationship generation unit performs inverse reinforcement learning on sales status information related to at least one of the following, which are included in the product placement information: sales for each product, age group and sex of purchasers for each product, duration of stay of purchasers in a store, and location of the store, to generate the relationship information in which at least one characteristic of a product, a purchaser, and a store is reflected;
Product placement determination device.
(Appendix 23)
A product placement determination device according to any one of appendixes 13 to 22,
The placement determination unit outputs the determined position of the product in the placement area.
Product placement determination device.
(Appendix 24)
A product placement determination device according to any one of appendixes 13 to 23,
The relationship generation unit outputs information based on the generated relationship information.
Product placement determination device.
(Appendix 25)
In the information processing device,
generating relationship information representing a relationship between a position in a placement area and the product based on product placement information representing a placement status of the product in a placement area in which the product is placed for sale and product information relating to the product;
determining a position of the product in the placement area based on the relationship information;
A computer-readable storage medium that stores a program for executing processing.

REFERENCE SIGNS LIST 10 Product placement determination device 11 Learning unit 12 Calculation unit 13 Determination unit 14 Output unit 16 Placement information storage unit 17 Weight information storage unit 18 Score storage unit 20 Input device 30 Display device 100 Product placement determination device 101 CPU
102 ROM
103 RAM
104 Program group 105 Storage device 106 Drive device 107 Communication interface 108 Input/output interface 109 Bus 110 Storage medium 111 Communication network 121 Relationship generation unit 122 Placement determination unit

Claims (10)

An information processing device,
generating relationship information representing a relationship between a position in a placement area and a product based on product placement information representing a placement status of the product in a placement area in which the product to be sold is placed and product information related to the product, generating the relationship information including weight information representing a weight for each position in the placement area for each product based on the product placement information and the product information;
generating evaluation information for evaluating a predetermined arrangement state of the products in the arrangement area based on the weight information included in the relationship information;
determining a position of the product in the placement area based on the evaluation information ;
How product placement is determined. An information processing device,
generating relationship information representing a relationship between a position in a placement area and a product based on product placement information representing a placement status of the product in a placement area in which the product to be sold is placed and product information related to the product, generating the relationship information including weight information representing a weight for a positional relationship between each product and each of the other products in the placement area based on the product placement information and the product information;
generating evaluation information for evaluating a predetermined arrangement state of the products in the arrangement area based on the weight information included in the relationship information;
determining a position of the product in the placement area based on the evaluation information ;
How product placement is determined. An information processing device,
generating relationship information representing a relationship between a position in a placement area and a product based on product placement information representing a placement status of the product in a placement area in which the product to be sold is placed and product information related to the product, generating the relationship information including weight information representing a weight for the placement area of each product in the placement area based on the product placement information and the product information;
generating evaluation information for evaluating a predetermined arrangement state of the products in the arrangement area based on the weight information included in the relationship information;
determining a position of the product in the placement area based on the evaluation information ;
How product placement is determined. An information processing device,
when generating relationship information representing a relationship between a position in a placement area and a product based on product placement information representing a placement status of the product in a placement area in which the product to be sold is placed and product information related to the product, generating the relationship information including weight information representing a weight corresponding to an appearance frequency of a preset placement status in the placement area for each product based on the product placement information and the product information;
generating evaluation information for evaluating a predetermined arrangement state of the products in the arrangement area based on the weight information included in the relationship information;
determining a position of the product in the placement area based on the evaluation information ;
How product placement is determined. The product placement determination method according to claim 2, 3 or 4 ,
The information processing device,
generating the relationship information including weight information representing a weight for each position in the placement area for each product based on the product placement information and the product information;
generating the evaluation information of a predetermined arrangement state of the products in the arrangement area based on the weight information;
How product placement is determined. The product placement determination method according to claim 1, 3 or 4 ,
The information processing device,
generating the relationship information including weight information representing a weight of a positional relationship between each product and each of the other products in the placement area based on the product placement information and the product information;
generating the evaluation information of a predetermined arrangement state of the products in the arrangement area based on the weight information;
How product placement is determined. The product placement determination method according to claim 1, 2 or 4 ,
The information processing device,
generating the relationship information including weight information representing a weight for a placement area in the placement region for each product based on the product placement information and the product information;
generating the evaluation information of a predetermined arrangement state of the products in the arrangement area based on the weight information;
How product placement is determined. The product placement determination method according to claim 1, 2 or 3 ,
The information processing device,
generating the relationship information including weight information representing a weight corresponding to an appearance frequency of a preset arrangement state in the arrangement area for each product based on the product arrangement information and the product information;
generating the evaluation information of a predetermined arrangement state of the products in the arrangement area based on the weight information;
How product placement is determined. A product placement determination method according to any one of claims 1 to 8 ,
The information processing device,
generating a plurality of pieces of evaluation information for each of a plurality of product placement situations in the placement area based on the weight information included in the relationship information;
determining a position of the product in the placement area specified by any one of the plurality of placement situations based on the plurality of pieces of evaluation information;
How product placement is determined. The product placement determination method according to claim 9 ,
The information processing device,
generating first evaluation information for one arrangement situation of the product in the arrangement area and second evaluation information for another arrangement situation different from the one arrangement situation based on the weight information included in the relationship information;
determining a position of the product in the placement area specified by a placement situation related to evaluation information having a larger value out of the first evaluation information and the second evaluation information;
How product placement is determined.

Images