JP2023159828A - Selection apparatus, selection method, and selection program - Google Patents

Abstract

To provide a selection apparatus, a selection method, and a selection program which select useful information for a user when the user is provided with information on a transaction target.SOLUTION: In a selection system 1, a selection apparatus 100 has a first selection unit 134 and a second selection unit 135. The first selection unit 134, based on user information relating to a user, selects a transaction object candidate as a candidate of a transaction object provided to a user from transaction objects. When the user displays predetermined content, the second selection unit 135 selects a transaction object to be displayed on the predetermined content from the transaction object candidates selected by the first selection unit 134, based on a score reflecting a first evaluation value obtained by using the number of times of displaying in a market with respect to each of the transaction object candidates.SELECTED DRAWING: Figure 6

Description

The present application relates to a selection device, a selection method, and a selection program.

Conventionally, techniques for providing various information to users are known. For example, there is a technology that uses a recommendation system to select content to be provided to a user based on the content viewed by the user.

Japanese Patent Application Publication No. 2016-051357

However, with the above-mentioned conventional technology, when providing transaction target information to a user, it is not always possible to select information that is useful for the user. For example, in the above-mentioned conventional technology, the content to be provided to the user is simply selected based on the user's content viewing using a recommendation system, and it is not possible to appropriately select information other than the content. is not limited.

The present application has been made in view of the above, and provides a selection device, a selection method, and a selection program that allow the user to select information that is beneficial to the user when providing transaction target information to the user. With the goal.

The selection device according to the present application includes a first selection section and a second selection section. The first selection unit selects a transaction object candidate, which is a candidate for a transaction object to be provided to the user, from among the transaction objects based on user information regarding the user. The second selection unit is configured to select a first selection unit, which is derived from among the transaction target candidates selected by the first selection unit, using the number of display times in the market for each transaction target candidate, when the user displays predetermined content. The transaction object to be displayed in the predetermined content is selected based on the score that reflects the evaluation value of.

According to one aspect of the embodiment, when providing transaction target information to a user, it is possible to select information that is useful for the user.

FIG. 1 is a diagram illustrating an example of a selection process executed by a selection device according to an embodiment. FIG. 2 is a diagram illustrating a specific example of the ranking of transaction targets according to the embodiment. FIG. 3 is a diagram showing a specific example of content according to the embodiment. FIG. 4 is a diagram showing an example of selection of transaction targets according to the embodiment. FIG. 5 is a diagram illustrating a specific example of the ranking of transaction target candidates according to the embodiment. FIG. 6 is a diagram illustrating an example of a selection system including a selection device according to an embodiment. FIG. 7 is a diagram illustrating an example of user information stored in the user information storage unit according to the embodiment. FIG. 8 is a diagram illustrating an example of transaction object information stored in the transaction object information storage unit according to the embodiment. FIG. 9 is a diagram illustrating an example of content information stored in the content storage unit according to the embodiment. FIG. 10 is a flowchart illustrating an example of a selection process procedure according to the embodiment. FIG. 11 is a hardware configuration diagram showing an example of a computer that implements the functions of the selection device according to the embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENTS A selection device, a selection method, and a mode for implementing a selection program (hereinafter referred to as an "embodiment") according to the present application will be described in detail below with reference to the drawings. Note that the selection device, selection method, and selection program according to the present application are not limited to this embodiment. Moreover, each embodiment can be combined as appropriate within the range that does not conflict with the processing contents. Further, in each of the embodiments below, the same parts are given the same reference numerals, and redundant explanations will be omitted.

[1. Overview of provision processing according to embodiment]
First, an example of the selection process executed by the selection device 100 will be described using FIG. 1. FIG. 1 is a diagram illustrating an example of a selection process executed by a selection device 100 according to an embodiment. FIG. 1 shows a selection process for a transaction object to be traded at an auction site or a flea market site. Here, the transaction object is the use of a product or service.

First, an example will be described in which content including a transaction object with a high score based on user information is provided. As shown in FIG. 1, the selection device 100 acquires user information regarding the user from the external device 20 (step S1).

In the example of FIG. 1, it is assumed that the user has registered as a member of a predetermined service. Furthermore, when registering as a member, the user shall register attribute information regarding attributes such as the user's gender, age, telephone number, and address. In this case, the selection device 100 acquires, as user information, user attribute information, purchase history on Internet shopping, online shopping malls, auction sites, flea market sites, etc., content display history, etc. from the external device 20. do. For example, the selection device 100 acquires user information from the external device 20 every predetermined period (eg, 5 hours, 1 day, etc.).

The selection device 100 also acquires information regarding the transaction object from the external device 20 (step S2). For example, the selection device 100 acquires information regarding a transaction object traded at an auction site or a flea market site from the external device 20. To give a more specific example, the selection device 100 acquires information regarding a smartphone being bid on at an auction site from the external device 20. For example, the selection device 100 acquires information regarding the transaction object from the external device 20 at predetermined intervals.

In the above example, the selection device 100 performs the acquisition process separately in step S1 and step S2, but the selection device 100 receives user information from the external device 20 and information regarding the transaction The information may also be acquired at the same time.

Subsequently, the selection device 100 receives a request for content provision from the terminal device 10 (step S3). For example, the selection device 100 receives a request to provide a portal site from the terminal device 10 as an example of content.

Note that in the above example, the selection device 100 performs step S3 after performing step S1 and step S2, but the selection device 100 performs step S1 after performing step S3. and step S2 may be executed.

Then, the selection device 100 calculates the first score SC1 of the selected transaction object (step S4). For example, the selection device 100 uses conventional technology such as machine learning to create a learning model that outputs a first score SC1 indicating the possibility of purchasing the transaction object by inputting the transaction object and user information. A first score SC1 is calculated using the generated model.

For example, the selection device 100 generates a learning model that has learned the relationship between a transaction object, user information, and information regarding whether the transaction object was purchased by the user in the past. Thereby, the selection device 100 calculates the first score SC1 by inputting the transaction object and the user information into the learning model.

Further, the selection device 100 acquires, as learning data, information indicating various types of user information (attributes, etc.) and information on a transaction target selected by the user. When the selection device 100 inputs the user information and the information of the transaction object selected by the user, the selection device 100 inputs the user information and the information of the transaction object not selected by the user. The learning model is trained to output a higher score than when Note that various other techniques for quantifying the degree to which a user shows interest in a transaction object can be employed as techniques used to calculate the score.

For example, the selection device 100 uses deep learning to perform a learning model generation process using a method such as backpropagation (error backpropagation method). For example, the selection device 100 uses a learning process to adjust the weight values that are taken into consideration when transmitting values between nodes. In this way, the selection device 100 generates a learning model through a process such as backpropagation that corrects the weight value so that the error between the output of the learning model and the correct answer corresponding to the input is reduced. For example, the selection device 100 generates a learning model by performing processing such as backpropagation to minimize a predetermined loss function. Thereby, the selection device 100 can perform the learning model generation process. Note that the calculation method is not limited to the above example, and various calculation methods can be adopted based on other machine learning.

Here, using FIG. 2, a specific example of the ranking based on the first score SC1 of the transaction object will be shown. FIG. 2 is a diagram illustrating a specific example of the ranking of transaction targets according to the embodiment. In the example of FIG. 2, a table TA1 is shown in which the ranking of the transaction object, the transaction object ID (Identifier) which is identification information for identifying the transaction object, and the first score SC1 are associated with each other. Here, it is assumed that table TA1 shows first scores SC1 for 20 transaction objects among the 50 transaction objects acquired from external device 20. In the example of FIG. 2, table TA1 shows an example of the first scores SC1 for the top 10 transaction objects among the first scores SC1 for the 20 transaction objects.

According to table TA1, the transaction object ID "M1" is ranked "1st" and the first score SC1 is "1.0". The transaction object ID "M2" is ranked "2nd", and the first score SC1 is "0.94". The transaction object ID "M3" is ranked "3rd" and the first score SC1 is "0.89". The transaction object ID "M4" is ranked "4th", and the first score SC1 is "0.84". The ranking "5th place" is the transaction object ID "M5", and the first score SC1 is "0.79".

Furthermore, the transaction object ID "M6" is ranked "6th" and the first score SC1 is "0.74". The ranking "7th place" is the transaction object ID "M7", and the first score SC1 is "0.68". The transaction object ID "M8" is ranked "8th place" and the first score SC1 is "0.63". The transaction object ID "M9" is ranked "9th place" and the first score SC1 is "0.58". The ranking "10th place" is the transaction object ID "M10", and the first score SC1 is "0.53".

In this manner, the selection device 100 determines the ranking of the transaction objects by calculating the first score SC1 for the transaction objects acquired from the external device 20. For example, the selection device 100 ranks the transaction objects in descending order of first score SC1. Further, the above calculation process may be executed every predetermined period or every step S3. Further, the selection device 100 can select a predetermined number of transaction objects as transaction object candidates starting from those with the highest first scores SC1.

Returning to the explanation of FIG. The selection device 100 selects a candidate for transaction based on the first score SC1 (step S5). For example, the selection device 100 selects the transaction object candidate with the highest ranking based on the ranking result of the transaction object candidates based on the first score SC1. Specifically, the selection device 100 selects the top 10 transaction objects with the highest first scores SC1 as transaction object candidates. In the example of FIG. 2, the selection device 100 selects transaction objects whose transaction object IDs are M1 to M10 as transaction object candidates.

Note that the above score calculation method is just an example, and the selection device 100 may calculate the product score using any method. For example, the selection device 100 may correct the product score by using a coefficient based on the evaluation of the vendor (store) that sells the product to the calculated product score. For example, the selection device 100 can correct the product score by multiplying the calculated product score by a coefficient based on the evaluation of the sales agency that sells the product.

Further, the selection device 100 provides content including, for example, the top six transaction target candidates ranked based on the first score SC1 (step S6). Here, content provided to the terminal device 10 will be explained using FIG. 3. FIG. 3 is a diagram showing a specific example of content according to the embodiment. For example, content C1 is content related to a portal site.

Further, the content C1 includes various information. For example, the content C1 includes a search window, information about news, information about content frequently displayed to users, advertising content, and the like.

Here, the content C1 includes an area AR1 as a predetermined display frame. In the example of FIG. 3, six transaction objects to be traded at online shop A, which is an auction service, are arranged in area AR1. In the example of FIG. 3, the transaction object is a smartphone. For example, in area AR1, the upper left row is "M1" with the ranking "1st", the upper middle row is "M2" with the ranking "2nd", the upper right row is "M3" with the ranking "3rd", and the lower left row is the ranking. The transaction targets are arranged in such order as "M4" for "4th place", "M5" for "5th place" in the lower middle row, "M6" for "6th place" in the lower right row, and so on.

In steps S1 to S6, an example has been described in which content including a transaction object with a high first score SC1 is provided. Next, the selection device 100 calculates a second score SC2 based on a predetermined formula (formula (1) described below), and provides content including a transaction object with a high second score SC2. An example of this will be explained in steps S7 to S11.

As shown in FIG. 1, the selection device 100 receives a request for content provision from the terminal device 10 (step S7). For example, the selection device 100 receives a request to provide a portal site from the terminal device 10 as an example of content.

Further, the selection device 100 acquires the number of display times for the transaction object from the terminal device 10 (step S8). For example, the selection device 100 acquires the number of times content is displayed and the number of times each transaction object is displayed from the terminal device 10. Here, the number of times of display is the number of times the content or information regarding the transaction object is displayed to the user.

Subsequently, the selection device 100 calculates a second score SC2 corresponding to the transaction target candidate based on the number of times of display (step S9). For example, the selection device 100 acquires at least one of the number of times content has been displayed to the user and the number of times information related to the transaction object has been displayed for each transaction object candidate from among the transaction object candidates. Then, a score is calculated that reflects the first evaluation value derived using the obtained number of times. For example, the selection device 100 uses a multi-armed bandit algorithm to calculate the second score SC2 corresponding to the transaction target candidate. To give a more specific example, the selection device 100 calculates a second score SC2 based on UCB (Upper Confidence Bound) using the following equation (1). Note that the second score SC2 may be calculated by further considering not only the first evaluation value but also the second evaluation value.

In the above formula (1), SC2 _u,i corresponds to the second score for user u's transaction object i, and SC1 _u,i corresponds to the first score for user u's transaction object i. do. Furthermore, in the above formula (1), SC-L _u,i corresponds to the local bonus (an example of the "second evaluation value") for transaction target i of user u, and SC-G _i is This corresponds to the global bonus (an example of the "first evaluation value") for transaction target i of user u.

SC-L _u,i in equation (1) is expressed by equation (2) below, and SC-G _i in equation (1) is expressed by equation (3) below.

In the above formula (2), T _U represents the number of times content is displayed, and N _TU (i) represents the number of times transaction target i is displayed at the T-th time. As shown in Equation (2) above, the local bonus SC-L is derived based on the relationship between the number of times a predetermined content is displayed and the number of times a transaction object is displayed through the predetermined content. The local bonus SC-L derived from the above equation (2) evaluates the novelty of the transaction object for the user. For example, the fewer times a transaction object is displayed, the larger the local bonus SC-L becomes, and the larger the local bonus SC-L, the more novel the transaction object is for the user.

In the above formula (3), _TC represents the number of times all transaction objects belonging to category C are displayed, and N _TC (i) represents the number of times transaction object i belonging to category C is displayed. As shown in formula (3) above, the global bonus SC-G is based on the relationship between the number of display times of all trade objects belonging to the category corresponding to the trade object candidate and the number of individual display times of each trade object belonging to the category. derived based on gender. The global bonus SC-G derived from the above equation (3) evaluates the novelty in the trading target market (eg, portal site, etc.). For example, the lower the number of times a transaction object is displayed in end category C, the larger the global bonus SC-G becomes, and the larger the global bonus SC-G, the more novel the transaction object is in the market. In other words, the global bonus SC-G is information that makes it possible to evaluate the degree to which each transaction candidate is recommended as a transaction target through predetermined content. Note that any granularity can be adopted as the granularity of the above-mentioned category C. For example, the granularity of electrical appliances, fashion items, etc., or the granularity of brands may be adopted.

The selection device 100 selects a transaction target to be displayed in the content based on the second score SC2 calculated by the above-mentioned formula (1), thereby selecting a transaction that takes into account novelty for the user and novelty in the market. Target selection can be achieved. FIG. 4 is a diagram showing an example of selection of transaction targets according to the embodiment.

FIG. 4 exemplifies a case where content recommending a transaction target is provided to user X. In FIG. 4, "camera" is shown as an example of the above-mentioned terminal category C, and each candidate for transaction belonging to the terminal category is cameras A, B, C, . . . .

In the case shown in FIG. 4, the selection device 100 selects cameras A, B, C, etc. as transaction object candidates that are transaction object candidates recommended to user X based on the first score SC1. Ru. As described above, based on the ranking result based on the first score SC1, for example, a transaction candidate with a high ranking is selected as a transaction candidate.

Next, the selection device 100 selects a transaction target to be recommended to the user X based on the second score SC2. For example, the local bonus SC-L that constitutes the second score SC2 narrows down the transaction targets that are new to the user X. In the case shown in FIG. 4, camera A and camera B, which have been recommended (displayed) to user X 0 times, are selected from among the plurality of cameras selected as transaction targets.

Furthermore, the global bonus SC-G constituting the second score SC2 selects a transaction object that is novel in the market. In the case shown in FIG. 4, among cameras A and B selected as novel transaction targets for user X, camera A is selected because it has been recommended 0 times to other users Y and Z. .

In this way, the selection device 100 can appropriately select a transaction object that is new to the user and novel in the market from among the transaction objects that have a high appeal to the user. Thereby, the selection device 100 can reduce the opportunity loss for the user to purchase the transaction object, and can lower the hurdle for bidding on the transaction object.

Here, using FIG. 5, a specific example of the ranking based on the second score SC2 of the candidate for transaction will be shown. FIG. 5 is a diagram illustrating a specific example of the ranking of transaction target candidates according to the embodiment.

According to Table TA2, the ranking "1st place" is the transaction target ID "M1", the first score SC1 is "1.0", the second score SC2 is "2.39", and the number of display times is "M1". It is "1.0". The ranking "2nd place" is the transaction target ID "M2", the first score SC1 is "0.94", the second score SC2 is "2.34", and the number of display is "1.0" It is. The ranking "3rd" is the transaction target ID "M3", the first score SC1 is "0.89", the second score SC2 is "2.30", and the number of display is "1.0" It is. The ranking "4th" is the transaction target ID "M4", the first score SC1 is "0.84", the second score SC2 is "2.24", and the number of display is "1.0" It is. The ranking “5th place” is the transaction target ID “M5”, the first score SC1 is “0.79”, the second score SC2 is “2.18”, and the number of display is “1.0” It is.

The ranking "6th place" is the transaction target ID "M6", the first score SC1 is "0.74", the second score SC2 is "2.13", and the number of display is "1.0" It is. The ranking “7th place” is the transaction target ID “M7”, the first score SC1 is “0.68”, the second score SC2 is “2.66”, and the number of display is “0” . The ranking "8th" is the transaction target ID "M8", the first score SC1 is "0.63", the second score SC2 is "2.60", and the number of display is "0" . The ranking "9th place" is the transaction target ID "M9", the first score SC1 is "0.58", the second score SC2 is "2.55", and the number of display times is "0". . The ranking “10th place” is the transaction target ID “M10”, the first score SC1 is “0.53”, the second score SC2 is “2.50”, and the number of display is “0” .

In this way, the selection device 100 calculates the second score SC2 for the transaction target candidate using the formula (1). Thereby, the selection device 100 determines the ranking of the transaction target candidates based on the second score SC2. For example, the selection device 100 ranks the transaction target candidates in descending order of second score SC2. Further, the above calculation process may be executed every predetermined period or every step S7.

Here, Table TA3 is shown with the ranking changed from Table TA2. Table TA3 is a table that associates the rankings of transaction object candidates, transaction object IDs, first scores SC1, second scores SC2, and display counts.

According to Table TA3, the ranking "1st place" is the transaction target ID "M7", the first score SC1 is "0.68", the second score SC2 is "2.66", and the number of display times is "M7". It is "1.0". The ranking "2nd" is the transaction target ID "M8", the first score SC1 is "0.63", the second score SC2 is "2.60", and the number of display is "1.0" It is. The ranking "3rd" is the transaction target ID "M9", the first score SC1 is "0.58", the second score SC2 is "2.55", and the number of display is "1.0" It is. The ranking "4th" is the transaction target ID "M10", the first score SC1 is "0.53", the second score SC2 is "2.50", and the number of display is "1.0" It is. The ranking "5th" is the transaction target ID "M11", the first score SC1 is "0.48", the second score SC2 is "2.45", and the number of display is "1.0" It is.

Further, the ranking "6th place" is the transaction target ID "M1", the first score SC1 is "1.0", the second score SC2 is "2.39", and the number of times of display is "2. 0". The ranking "7th place" is the transaction target ID "M12", the first score SC1 is "0.43", the second score SC2 is "2.37", and the number of display is "0". . The ranking "8th" is the transaction target ID "M13", the first score SC1 is "0.38", the second score SC2 is "2.36", and the number of display is "0" . The ranking "9th place" is the transaction target ID "M14", the first score SC1 is "0.33", the second score SC2 is "2.35", and the number of display times is "0". . The ranking "10th" is the transaction target ID "M2", the first score SC1 is "0.94", the second score SC2 is "2.34", and the number of display is "2.0" It is. Note that the number of times of display shown in table TA3 is calculated by adding 1 to the number of times of display shown in table TA2, assuming that "1st place" to "6th place" are displayed.

Returning to the explanation of FIG. The selection device 100 selects a transaction target based on the second score SC2 (step S10). For example, the selection device 100 selects the top six transaction targets with the highest second scores SC2 based on the second scores SC2. In the example of FIG. 5, the selection device 100 selects transaction objects whose transaction object IDs are "M7," "M8," "M9," "M10," "M11," and "M1."

Subsequently, the selection device 100 provides content including the transaction object (step S11). In the example of FIG. 5, the selection device 100 selects the six transaction targets "M7", "M8", "M9", "M10", "M11", and "M1" from the order of the highest second scores. The terminal device 10 is provided with content related to a portal site whose target is arranged in a predetermined display frame.

Then, the selection device 100 executes a selection process of selecting a transaction target based on the number of times the content provided in step S11 is displayed. That is, after executing step S11, the selection device 100 repeats the processes shown in steps S7 to S11.

Note that in the above example, the selection device 100 provides content including a transaction object with a high first score SC1 in steps S1 to S6, but the present invention is not limited to the above example. For example, the selection device 100 may calculate the second score SC2 based on the above-mentioned formula (1) in steps S1 to S6, and provide content including a transaction object with a high second score SC2.

Furthermore, when calculating the second score SC2, the selection device 100 may weight the local bonus SC-L and the global bonus SC-G, respectively. In this case, the selection unit 100 may perform weighting so that the local bonus SC-L is prioritized over the global bonus SC-G. Thereby, the selection device 100 can select information with priority given to the recommendation status for the user.

Conventionally, when providing transaction information to a user, there have been cases in which it cannot be said that the user can select information that is beneficial to the user. In contrast, when the user displays predetermined content, the selection device 100 according to the embodiment calculates the number of display times in the market for each trading target candidate selected from among the trading target candidates selected based on user information. By selecting the transaction object to be displayed in a predetermined content based on the score that reflects the global bonus derived using the system, it is possible to select information that is useful to the user and solve the conventional problems.

For example, the selection device 100 selects information on a product (trade target) that the user is likely to desire but is unknown to the user by selecting the trade target based on the score that reflects the local bonus. This makes it possible to provide services in a timely manner, thereby preventing opportunity loss for users. Thereby, for example, even if the item that the user is looking for on the auction site is sold by another user, it is possible to provide an alternative, such as providing information on other identical or similar items. In addition, the selection device 100 selects the transaction target based on the score that reflects the global bonus, so that if there is information on multiple products that the user is likely to desire but do not know about, the selection device 100 can It becomes possible to select and provide information on newer products. As a result, it is possible to select and provide information that is useful to the user, such as selecting information on products that the user is likely to win at an auction site. In this way, the selection device 100 can select information useful to the user.

Note that the selection device 100 may calculate the second score SC2 that reflects only the global bonus. Thereby, the selection device 100 can select a transaction target by focusing only on novelty in the market.

[2. Selection system configuration]
A configuration example of the selection system 1 according to the embodiment will be described using FIG. 6. FIG. 6 is a diagram illustrating an example of a selection system including a selection device according to an embodiment. As shown in FIG. 6, the selection system 1 includes a terminal device 10, an external device 20, and a selection device 100. The terminal device 10, the external device 20, and the selection device 100 are connected via a network N so that they can communicate by wire or wirelessly. Note that the selection system 1 shown in FIG. 6 may include a plurality of terminal devices 10, a plurality of external devices 20, and a plurality of selection devices 100.

The terminal device 10 according to the embodiment is an information processing device used by a user who accesses content such as a web page displayed on a browser or content for an application. For example, the terminal device 10 is a desktop PC (Personal Computer), a notebook PC, a tablet terminal, a mobile phone, a PDA (Personal Digital Assistant), or the like. Note that the terminal device 10 is not limited to the above example, and may be, for example, a smart watch or a wearable device.

The external device 20 according to the embodiment is an information processing device that provides various information, and is realized by, for example, a server device, a cloud system, or the like. For example, the external device 20 provides user information as various information. In addition, as another example, the external device 20 may be used for Internet shopping, an electronic shopping mall, a flea market site, an auction site, a travel or restaurant reservation site, a credit card contract site, a financial product providing site, etc. Provide information regarding transaction objects.

The selection device 100 according to the embodiment is an information processing device that can communicate with various devices via a predetermined network N such as the Internet, and is realized by, for example, a server device or a cloud system. For example, the selection device 100 is communicably connected to various other devices via the network N.

[3. Configuration of selected device]
A configuration example of the selection device 100 according to the embodiment will be described using FIG. 6. As shown in FIG. 6, the selection device 100 includes a communication section 110, a storage section 120, and a control section 130.

(About communication department 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card). The communication unit 110 is connected to the network N by wire or wirelessly. The selection device 100 transmits and receives information to and from other devices such as the terminal device 10 and the external device 20 via the network N.

(About storage unit 120)
The storage unit 120 is realized by, for example, a RAM (Random Access Memory), a semiconductor memory element such as a flash memory, or a storage device such as a hard disk or an optical disk. For example, the storage unit 120 includes a user information storage unit 121, a transaction target information storage unit 122, and a content storage unit 123.

(User information storage unit 121)
The user information storage unit 121 stores various types of user information regarding users. Here, FIG. 7 shows an example of user information stored in the user information storage unit 121 according to the embodiment. In the example shown in FIG. 7, the user information stored in the user information storage unit 121 includes items such as "user ID" and "user information." These items included in the user information are associated with each other. Note that in FIG. 7, the information stored in each item of user information is simplified and expressed as an abstract concept. Further, FIG. 7 shows an example of user information according to the embodiment, and may include information other than that shown in FIG. 7.

``User ID'' is an identifier that identifies a user. "Attribute information" is attribute information regarding the attributes of the user associated with the "user ID." For example, the attribute information includes the user's age, gender, telephone number, address, etc.

"Purchase history" is a history of purchases made by the user associated with the "user ID." For example, the purchase history includes information regarding transaction objects purchased by the user, the type of transaction object, the number of times the transaction object was purchased, the date and time when the transaction object was purchased, and the like.

The "display history" is a history of contents displayed by the user associated with the "user ID." For example, the display history includes information regarding content displayed by the user, the type of content, the number of times the content was displayed, the date and time the content was displayed, and the like.

For example, in FIG. 7, "U1" identified by the user ID has attribute information "CH1", purchase history "PH1", and display history "WA1". In the example shown in Figure 6, the attribute information, etc. is expressed using abstract codes such as "CH1", but the attribute information, etc. can be expressed by specific numerical values, specific character strings, or various information. It may be a file format that includes.

(About transaction target information storage unit 122)
The transaction object information storage unit 122 stores various transaction object information regarding transaction objects. Here, FIG. 8 shows an example of transaction target information stored in the transaction target information storage unit 122 according to the embodiment. In the example shown in FIG. 8, the transaction object information stored in the transaction object information storage unit 122 is managed for each user, and includes "transaction object ID,""transactionobject,""firstscore,""secondscore," and "second score." Includes items such as "Score" and "Number of Views." In addition, in FIG. 8, the information stored in each item included in the transaction target information is simplified and expressed as an abstract concept. Further, FIG. 8 shows an example of transaction target information according to the embodiment, and may include information other than that shown in FIG. 8.

“Transaction object ID” is an identifier that identifies a transaction object. The “transaction object” is information regarding the transaction object associated with the “transaction object ID”. The "first score" is the first score associated with the "transaction object ID."

“Second score” is a second score associated with “transaction object ID”. The "number of times of display" is the number of times information regarding the transaction object associated with the "transaction object ID" has been displayed to the user.

For example, in FIG. 8, for "M1" identified by the transaction object ID, the transaction object is "MA1", the first score is "1.0", and the second score is "2.4". , the display count is "1.0". In the example shown in FIG. 7, the transaction object is expressed by an abstract code such as "MA1," but the transaction object may be in a file format containing various information regarding the transaction object.

(About content storage unit 123)
The content storage unit 123 stores information regarding content. Here, FIG. 9 shows an example of content information stored in the content storage unit 123 according to the embodiment. In the example shown in FIG. 9, the content information stored in the content storage unit 123 includes items such as "content ID" and "content." Note that in FIG. 9, the information stored in each item of content information is simplified and expressed as an abstract concept. Further, FIG. 9 shows an example of content information according to the embodiment, and may include information other than that shown in FIG. 9.

"Content ID" is an identifier that identifies content. “Content” is information regarding the content associated with “Content ID”. Specifically, the content may indicate information regarding the contents of the content.

For example, the content is content related to a portal site. Furthermore, as another example, the content may be content related to a news site, an auction site, a weather forecast site, a shopping site, a finance (stock price) site, or the like.

Further, the content may be content related to a route search site, a map providing site, a travel site, a restaurant introduction site, a web blog site, an SNS (Social Networking Service) site, or the like.

For example, in FIG. 9, the content of "C1" identified by the content ID is "CO1." Note that in the example shown in Figure 9, the content, etc. is expressed by an abstract code such as "CO1", but the content, etc. may be a file containing specific numerical values, specific character strings, or various information. It may be a format, etc.

(About the control unit 130)
The control unit 130 is realized by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like executing various programs stored in a storage device inside the selection device 100 using the RAM as a work area. Further, the control unit 130 is realized by, for example, an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

The control unit 130 shown in FIG. 6 includes an acquisition unit 131, a reception unit 132, a calculation unit 133, a first selection unit 134, a second selection unit 135, and a provision unit 136. , realizes or executes the information processing functions and operations described below. Note that the control unit 130 may have a plurality of divided internal configurations in processing units that realize or execute information processing functions and actions described below. The configuration is not limited to that shown in FIG. 6, and may be any other configuration as long as it performs information processing to be described later.

(Acquisition unit 131)
The acquisition unit 131 acquires various information. Specifically, the acquisition unit 131 acquires user information regarding the user from the external device 20. In the example of FIG. 1, the acquisition unit 131 collects, as user information, user attribute information, purchase history on Internet shopping, online shopping malls, auction sites, flea market sites, etc., content display history, etc. from an external device. Obtained from 20. The acquisition unit 131 then stores this user information in the user information storage unit 121. For example, the acquisition unit 131 acquires user information from the external device 20 at predetermined intervals, and updates the user information stored in the user information storage unit 121.

Further, the acquisition unit 131 acquires information regarding the transaction object from the external device 20. For example, the acquisition unit 131 acquires information regarding a transaction object traded at an auction site or a flea market site from the external device 20. To give a more specific example, the acquisition unit 131 acquires from the external device 20 information regarding a smartphone that is being bid on at an auction site. Then, the acquisition unit 131 stores information regarding the transaction target in the transaction target information storage unit 122. For example, the acquisition unit 131 acquires information regarding the transaction object from the external device 20 every predetermined period, and updates the information regarding the transaction object stored in the transaction object information storage unit 122.

The acquisition unit 131 also acquires the number of times the transaction object is displayed from the terminal device 10 . For example, the acquisition unit 131 acquires the number of times content is displayed and the number of times each transaction object is displayed from the terminal device 10. Further, the acquisition unit 131 acquires the content as well as control information provided in advance to acquire the display count. Then, the acquisition unit 131 stores the number of times of display in the transaction target information storage unit 122.

(Reception Department 132)
The reception unit 132 receives various requests. Specifically, the receiving unit 132 receives a request for content provision from the terminal device 10. For example, the receiving unit 132 receives a request for providing a portal site from the terminal device 10 as an example of predetermined content.

(Calculation unit 133)
The calculation unit 133 calculates various scores. Specifically, the calculation unit 133 calculates the first score SC1 of the transaction object acquired by the acquisition unit 131. For example, the calculation unit 133 uses conventional technology such as machine learning to create a learning model that outputs a first score SC1 indicating the possibility of purchasing the transaction object by inputting the transaction object and user information. generate.

For example, the calculation unit 133 generates a learning model that has learned the relationship between the transaction object, user information, and information regarding whether the transaction object was purchased by the user in the past. Thereby, the calculation unit 133 calculates the first score SC1 by inputting the transaction object and the user information into the learning model. Then, the calculation unit 133 stores the first score SC1 in the transaction target information storage unit 122.

Further, the calculation unit 133 acquires, as learning data, information indicating various types of user information and information on a transaction target selected by the user. Then, when the user information and the information on the transaction object selected by the user are input, the calculation unit 133 inputs the user information and the information on the transaction object not selected by the user. The learning model is trained to output a higher score than when In addition, various other techniques can be employed to quantify the degree of interest shown by the user in the transaction object.

For example, the calculation unit 133 uses deep learning to generate a learning model using a method such as backpropagation. For example, the calculation unit 133 uses a learning process to adjust the weight values that are taken into consideration when transmitting values between nodes. In this way, the calculation unit 133 generates a learning model through a process such as backpropagation that corrects the weight values so that the error between the output of the learning model and the correct answer corresponding to the input is reduced. For example, the calculation unit 133 generates a learning model by performing processing such as backpropagation to minimize a predetermined loss function. Thereby, the calculation unit 133 can perform the learning model generation process. Note that the calculation method is not limited to the above example, and various calculation methods can be adopted based on other machine learning.

In this manner, the calculation unit 133 determines the ranking of the transaction objects by calculating the first score SC1 for the transaction objects acquired from the external device 20. For example, the calculation unit 133 ranks the transaction objects in descending order of the first score SC1. Further, the above calculation process may be executed every predetermined period. Further, the above calculation process may be executed, for example, every step S3 shown in the example of FIG.

(First selection unit 134)
The first selection unit 134 selects transaction object candidates from among the transaction objects based on the user information. For example, the first selection unit 134 may rank the transaction target candidates based on a predetermined criterion, and select the transaction target candidate with a higher ranking. Specifically, the first selection unit 134 selects the top 10 transaction objects with the highest first scores SC1 as transaction object candidates based on the first score SC1. For example, according to FIG. 2 described above, an example is shown in which the first selection unit 134 selects transaction objects whose transaction object IDs are M1 to M10 as transaction object candidates.

(Second selection unit 135)
When the user displays the predetermined content, the second selection unit 135 selects the transaction targets displayed in the predetermined content from among the transaction target candidates selected by the first selection unit 134. Based on the second score SC2 (for example, see formula (1) above) that reflects the global bonus (an example of the "first evaluation value") derived using the number of times the item is displayed in the market, Select the transaction object to be displayed. For example, the second selection unit 135 calculates the number of display times of all transaction targets belonging to the category corresponding to the transaction target candidate among the transaction target candidates selected by the first selection unit 134, and the display count of each transaction target belonging to the category. A global bonus can be derived based on the relationship with the number of impressions. The global bonus evaluates novelty in the market being traded (eg, portal site, etc.). A specific calculation method is shown in the above-mentioned formula (3).

Further, the second selection unit 135 derives a local bonus based on the relationship between the number of times the predetermined content is displayed and the number of times the transaction target is displayed through the predetermined content, and a second score further reflects the derived local bonus. Based on SC2, a transaction object to be displayed in a predetermined content can be selected. The local bonus evaluates the novelty of the transaction object for the user. A specific calculation method is shown in the above-mentioned formula (2).

For example, the second selection section 135 selects the top six transaction objects with the highest second score SC2 from the transaction object information storage section 122 based on the second score SC2. According to FIG. 5 described above, the second selection unit 135 selects transaction objects whose transaction object IDs are "M7", "M8", "M9", "M10", "M11", and "M1". It is shown.

Further, the second selection unit 135 may weight the global bonus and the local bonus, and select the transaction object to be displayed in the predetermined content based on the weighted second score SC2. In this case, the second selection unit 135 may perform weighting so that the second evaluation value is prioritized over the first evaluation value.

In this way, the second selection unit 135 uses the multi-armed banded algorithm to select from among the transaction target candidates each time the user accesses the content so that the transaction target with a small number of display times is preferentially displayed. Reselect the transaction target. Thereby, the calculation unit 133 can appropriately select a transaction target that is highly appealing to users. In addition, the calculation unit 133 appropriately selects a transaction object that is not displayed to the user from among the transaction objects that have a high appeal effect to the user, so that the calculation unit 133 reduces the opportunity loss of the user to purchase the transaction object. can be reduced.

(About the providing unit 136)
The providing unit 136 provides various information. For example, the providing unit 136 refers to the content storage unit 123 and provides the terminal device 10 with content including the top six transaction target candidates ranked based on the first score SC1. In the example of FIG. 2, the providing unit 136 provides content in which six transaction objects "M1" to "M6" from the order of the highest first score SC1 are arranged in a predetermined display frame, Content related to the portal site is provided to the terminal device 10.

Further, the providing unit 136 refers to the content storage unit 123 and provides content including transaction objects ranked based on the second score SC2. In the example of FIG. 5, the providing unit 136 performs transactions for six transaction targets "M7", "M8", "M9", "M10", "M11", and "M1" from the order of the highest second scores. The terminal device 10 is provided with content related to a portal site whose target is arranged in a predetermined display frame.

[4. Processing procedure]
Hereinafter, the procedure of the selection process executed by the selection device 100 according to the embodiment will be described. FIG. 10 is a flowchart illustrating an example of a selection process procedure according to the embodiment. The selection processing procedure shown in FIG. 10 is executed by the control unit 130 of the selection device 100. The selection processing procedure shown in FIG. 10 is repeatedly executed while the selection device 100 is in operation.

As shown in FIG. 10, the acquisition unit 131 acquires user information from the external device 20 (step S101). The acquisition unit 131 also acquires information regarding the transaction object from the external device 20 (step S102).

Subsequently, when the reception unit 132 receives the content provision request, the first selection unit 134 provides the content to the user from among the transaction targets based on the user information acquired by the acquisition unit 131. A transaction target candidate is selected (step S103).

Further, when the user displays the predetermined content, the second selection unit 135 selects the transaction target displayed in the predetermined content from among the transaction target candidates selected by the first selection unit 134 for each transaction. A transaction object to be displayed in a predetermined content is selected based on a score reflecting a first evaluation value derived using the number of times the object candidate is displayed in the market (step S104).

Next, the determining unit 134 determines offered products to be included in the search results to be provided to the user, based on the modified search results (step S104).

Further, the providing unit 136 provides the terminal device 10 with content including a predetermined number of transaction objects selected by the second selection unit 135. Specifically, the providing unit 136 provides the terminal device 10 with content in which a predetermined number of transaction objects are arranged in a predetermined display frame (step S105).

[5. Modified example]
The selection device 100 described above may be implemented in various different forms other than the above embodiment. Therefore, other embodiments of the selection device 100 will be described below.

(5-1. Regarding the source of the number of impressions)
In the embodiment described above, the selection device 100 acquires the number of times content is displayed and the number of times a transaction object is displayed from the terminal device 10, but these display times are not displayed on the auction site or flea market site with which the selection device 100 cooperates. Not limited to number of times. For example, the display count acquired by the selection device 100 may be another auction site or flea market site. Further, the number of times of display may be the number of times a search result is displayed on a search site.

(5-2. About users)
In the embodiment described above, a user who is registered as a member of a predetermined service is used as an example of a destination to which content is provided by the selection device 100, but the present invention is not limited to the above example. For example, the user may be any user, such as a user who is not registered as a member of a predetermined service.

(5-3. Regarding transaction objects)
In the embodiment described above, the transaction object that the selection device 100 recommends to the user through the content is an example of a transaction object traded on an auction site or a flea market site, but the selection device 100 does not need to be limited to the above example. For example, the transaction object may be any transaction object. For example, the transaction object may be a transaction object whose quantity is limited. Further, the transaction object may be a transaction object that, when purchased by a first user, cannot be purchased by a second user who is a user different from the first user. Further, the transaction object may be a transaction object traded through Internet shopping, an electronic shopping mall, or the like.

(5-4. Provision of information regarding transaction targets)
In the above embodiment, an example has been described in which the selection device 100 provides the user with content including a transaction target, but the selection device 100 does not need to be limited to the above example. For example, an external server that cooperates with the selection device 100 may transmit the content to the terminal device 10 to provide the content to the user. In this case, the selection device 100 may provide information regarding the transaction object to the external server.

(5-5. Regarding transaction target information)
In the above embodiment, an example has been described in which the transaction object information stored in the transaction object information storage section 122 includes the number of times of display, but the present invention is not limited to the above example. For example, the transaction object information stored in the transaction object information storage section 122 may not include the number of times of display. In this case, the user information storage unit 121 may store the number of times of display for each user as part of the user information. Note that various methods can be used to manage the number of times of display. For example, the number of display times stored by the user information storage unit 121 as part of the user information may be managed for each user as a display history.

[6. Hardware configuration]
Further, the selection device 100 according to the present embodiment described above is realized by, for example, a computer 1000 having a configuration as shown in FIG. 11. FIG. 11 is a hardware configuration diagram showing an example of a computer that implements the functions of the selection device according to the embodiment.

The computer 1000 is connected to an output device 1010 and an input device 1020, and has an arithmetic device 1030, a primary storage device 1040, a secondary storage device 1050, an output IF (Interface) 1060, an input IF 1070, and a network IF 1080 connected by a bus 1090. has.

The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050, programs read from the input device 1020, and performs various processes. The primary storage device 1040 is a memory device such as a RAM that temporarily stores data used by the arithmetic device 1030 for various calculations. Further, the secondary storage device 1050 is a storage device in which data used by the arithmetic unit 1030 in various calculations and various databases are registered, and is realized by a ROM (Read Only Memory), an HDD, a flash memory, or the like.

The output IF 1060 is an interface for transmitting information to be output to the output device 1010 that outputs various information such as a monitor or printer, and is, for example, a USB (Universal Serial Bus), a DVI (Digital Visual Interface), This is realized using a connector compliant with standards such as HDMI (registered trademark) (High Definition Multimedia Interface). Further, the input IF 1070 is an interface for receiving information from various input devices 1020 such as a mouse, a keyboard, and a scanner, and is realized by, for example, a USB.

Note that the input device 1020 is, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a PD (Phase change rewritable disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), or a tape. It may be a device that reads information from a medium, a magnetic recording medium, a semiconductor memory, or the like. Furthermore, the input device 1020 may be an external storage medium such as a USB memory.

Network IF 1080 receives data from other devices via network N and sends it to computing device 1030, and also sends data generated by computing device 1030 to other devices via network N.

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

For example, when the computer 1000 functions as the selection device 100 according to the embodiment, the arithmetic device 1030 of the computer 1000 controls the control unit 130 by executing a program (for example, a selection program) loaded on the primary storage device 1040. Achieves the same functionality as . That is, the arithmetic device 1030 realizes the processing by the selection device 100 according to the embodiment by cooperating with a program (for example, a selection program) loaded onto the primary storage device 1040.

[7. others]
Among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed manually. Part of this can also be done automatically using known methods. In addition, information including the processing procedures, specific names, and various data and parameters shown in the above documents and drawings may be changed arbitrarily, unless otherwise specified.

Furthermore, each component of each device shown in the drawings is functionally conceptual, and does not necessarily need to be physically configured as shown in the drawings. In other words, the specific form of distributing and integrating each device is not limited to what is shown in the diagram, and all or part of the devices can be functionally or physically distributed or integrated in arbitrary units depending on various loads and usage conditions. Can be integrated and configured.

Further, each of the embodiments described above can be combined as appropriate within a range that does not conflict with the processing contents.

[8. effect]
The selection device 100 according to the embodiment includes a first selection section 134 and a second selection section 135. The first selection unit 134 selects a transaction object candidate, which is a candidate for a transaction object to be provided to the user, from among the transaction objects based on user information regarding the user. When the user displays predetermined content, the second selection unit 135 selects a candidate for transaction from among the candidates for transaction selected by the first selection unit 134, using the number of times each candidate for transaction is displayed in the market. A transaction object to be displayed in predetermined content is selected based on a score that reflects the first evaluation value (global bonus).

In addition, in the selection device 100 according to the embodiment, the second selection unit 135 displays the number of display times of all transaction objects belonging to the category corresponding to the transaction object candidate among the transaction object candidates, and the individual transaction object of each transaction object belonging to the category. A first evaluation value (global bonus) is derived based on the relationship with the number of display times.

Furthermore, in the selection device 100 according to the embodiment, the second selection unit 135 derives a second evaluation value based on the relationship between the number of times the predetermined content is displayed and the number of times the transaction target is displayed through the predetermined content. , selects a transaction object to be displayed in the predetermined content based on a score that further reflects the derived second evaluation value (local bonus).

For this reason, when the selection device 100 according to the embodiment provides transaction target information to the user by the processing executed by each of the above-mentioned units or by any combination of the units, the selection device 100 according to the embodiment Users can select information that is useful to them. For example, the selection device 100 selects information on a product (trade target) that the user is likely to desire but is unknown to the user by selecting the trade target based on the score that reflects the local bonus. This makes it possible to provide services in a timely manner, thereby preventing opportunity loss for users. Thereby, for example, even if the item that the user is looking for on the auction site is sold by another user, it is possible to provide an alternative, such as providing information on other identical or similar items. In addition, the selection device 100 selects the transaction target based on the score that reflects the global bonus, so that if there is information on multiple products that the user is likely to desire but do not know about, the selection device 100 can It becomes possible to select and provide information on newer products. As a result, it is possible to select and provide information that is useful to the user, such as selecting information on products that the user is likely to win at an auction site. In this way, the selection device 100 can select information useful to the user.

In the selection device 100 according to the embodiment, the second selection unit 135 weights the first evaluation value (global bonus) and the second evaluation value (local bonus), and , a transaction object to be displayed in a predetermined content may be selected based on a score reflecting a first evaluation value (global bonus) and a second evaluation value (local bonus).

Furthermore, in the selection device 100 according to the embodiment, the second selection unit 135 may perform weighting so that the second evaluation value is prioritized over the first evaluation value.

In this way, the selection device 100 according to the embodiment can select information with priority given to the recommendation status for the user by the processing executed by each of the units described above, or by a combination of any of the units.

Furthermore, in the selection device 100 according to the embodiment, the first selection unit 134 may rank the transaction object candidates based on a predetermined criterion, and select the transaction object candidate with a higher ranking.

In this way, the selection device 100 according to the embodiment selects information that is new in the market and is not very useful to the user, by the processing executed by each of the above-mentioned units or by a combination of the units. This can reduce the possibility of

Further, the selection device 100 according to the embodiment further includes a providing unit 136 that provides content including a predetermined number of transaction objects selected by the second selection unit 135.

Furthermore, in the selection device 100 according to the embodiment, the providing unit 136 may provide content in which a predetermined number of transaction targets are arranged in a predetermined display frame.

In this way, the selection device 100 according to the embodiment can display, for example, information regarding a highly appealing transaction object in a predetermined display frame by the processing executed by each of the above-mentioned units or by a combination of the units. It is possible to provide laid out content to users.

As mentioned above, the embodiments of the present application have been described in detail based on several drawings, but these are merely examples, and various modifications and variations can be made based on the knowledge of those skilled in the art, including the embodiments described in the disclosure section of the invention. It is possible to carry out the invention in other forms with modifications.

Further, the above-mentioned "section, module, unit" can be read as "means", "circuit", etc. For example, the control section can be read as a control means or a control circuit.

N Network 1 Selection system 10 Terminal device 20 External device 100 Selection device 110 Communication unit 120 Storage unit 121 User information storage unit 122 Transaction target information storage unit 123 Content storage unit 130 Control unit 131 Acquisition unit 132 Reception unit 133 Calculation unit 134 1 selection section 135 2nd selection section 136 provision section

Claims (9)

a first selection unit that selects a transaction object candidate that is a candidate for a transaction object to be provided to the user from among the transaction objects based on user information regarding the user;
When the user displays predetermined content, a first evaluation value derived using the number of times each trading target candidate is displayed in the market is calculated from among the trading target candidates selected by the first selection unit. A selection device comprising: a second selection unit that selects a transaction object to be displayed in the predetermined content based on the reflected score. The second selection section includes:
Among the transaction target candidates, the number of display times of all transaction targets belonging to the category corresponding to the transaction target candidate and the individual number of display times of each transaction target belonging to the category are determined. The selection device according to claim 1, wherein an evaluation value of 1 is derived. The second selection section includes:
A second evaluation value is derived based on the relationship between the number of times the predetermined content is displayed and the number of times the transaction object is displayed through the predetermined content, and the score further reflects the derived second evaluation value. The selection device according to claim 2, wherein a transaction object to be displayed in the predetermined content is selected based on the selected content. The second selection section includes:
The first evaluation value and the second evaluation value are weighted, and the predetermined score is calculated based on the score reflecting the weighted first evaluation value and the second evaluation value. 4. The selection device according to claim 3, wherein the selection device selects a transaction object to be displayed in the content. The first selection section includes:
The selection device according to claim 1, wherein the transaction target candidates are ranked based on predetermined criteria, and the transaction target candidate with a higher ranking is selected. The selection device according to claim 1, further comprising a providing unit that provides content including a predetermined number of transaction objects selected by the second selection unit. The provision department is
The selection device according to claim 6, wherein the predetermined number of transaction objects provides content arranged in a predetermined display frame. An information processing method performed by a computer, the method comprising:
a first selection step of selecting transaction object candidates that are candidates for transaction objects to be provided to the user from among the transaction objects based on user information about the user;
When the user displays predetermined content, a first evaluation value derived using the number of times each trading target candidate is displayed in the market is calculated from among the trading target candidates selected in the first selection step. and a second selection step of selecting a transaction object to be displayed in the predetermined content based on the reflected score. to the computer,
a first selection step of selecting a transaction object candidate that is a candidate for a transaction object to be provided to the user from among the transaction objects based on user information about the user;
When the user displays predetermined content, a first evaluation value derived using the number of times each trading target candidate is displayed in the market is calculated from among the trading target candidates selected by the first selection procedure. and a second selection step of selecting a transaction object to be displayed in the predetermined content based on the reflected score.

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