JP2023121108A - Information processing device, information processing method, and information processing program - Google Patents

Abstract

To more properly provide information corresponding to a search query.SOLUTION: An information processing device comprises an acceptance unit, an estimation unit, a determination unit, and a provision unit. The acceptance unit accepts a search query. The determination unit determines a display format of plurality-of-element information which is information in which a plurality of elements according to the search query are arranged, on the basis of an intention of the search query accepted by the acceptance unit. The provision unit provides the plurality-of-element information of the display format determined by the determination unit.SELECTED DRAWING: Figure 1

Description

Application for application of Article 30, Paragraph 2 of the Patent Law has been filed Address published on November 9, 2021 https://topics. shopping. Yahoo. co. jp/notice/archives/202111102021. html Address published on November 19, 2021 https://store-info. Yahoo. co. jp/shopping/topics/tool2021/test/211119-3. html Posted on December 3, 2021 and public address https://appstoreconnect. apple. com/apps/446016180/appstore/reviewsubmissions/details/e9ae2241-e0f9-4960-9cf0-94d64f0527bf https://apps. apple. com/jp/app/yahoo-%E3%82%B7%E3%83%A7%E3%83%83%E3%83%94%E3%83%B3%E3%82%B0/id446016180 Reiwa 4 January 6 posting address https://play. google. com/console/u/0/developers/9059031076674808747/app/4972974430624390735/tracks/4698240759174279260/releases/278/details

The present invention relates to an information processing device, an information processing method, and an information processing program.

Conventionally, techniques for providing various information to users are known. For example, there is known a technique of providing users with ranking information in which transaction targets such as product information corresponding to a search query are used as ranking factors (for example, Patent Literature 1).

Japanese Patent No. 6776072

However, the above conventional techniques have room for improvement. For example, in the conventional technology described above, it is possible to provide information in which elements corresponding to a search query are arranged, but there is room for improvement in the manner in which information corresponding to a search query is provided.

The present application has been made in view of the above, and an object thereof is to provide an information processing device, an information processing method, and an information processing program capable of more appropriately providing information corresponding to a search query.

An information processing apparatus according to the present application includes a reception unit, an estimation unit, a determination unit, and a provision unit. The reception unit receives search queries. The decision unit decides, based on the intent of the search query received by the reception unit, the display format of the multiple-element information, which is information in which multiple elements corresponding to the search query are arranged. The providing unit provides the multiple-element information in the display format determined by the determining unit.

According to one aspect of the embodiment, it is possible to more appropriately provide information corresponding to a search query.

FIG. 1 is a diagram for explaining an information processing method performed by an information processing apparatus according to an embodiment. FIG. 2 is a diagram illustrating an example of content provided to a user from the information processing apparatus according to the embodiment; FIG. 3 is a diagram illustrating an example of segmentation of scores indicating the degree of relevance of a search query to each of categories and brands in the information processing apparatus according to the embodiment. FIG. 4 is a diagram illustrating an example of case classification determination information held by the information processing apparatus according to the embodiment. FIG. 5 is a diagram illustrating an example of ranking information determination information possessed by the information processing apparatus according to the embodiment. FIG. 6 is a diagram showing an example of ranking of transaction targets from multiple perspectives in the information processing apparatus according to the embodiment. FIG. 7 is a diagram illustrating an example of search results and ranking information provided by the information processing apparatus according to the embodiment; FIG. 8 is a diagram illustrating another example of ranking information provided by the information processing device according to the embodiment. FIG. 9 is a diagram illustrating a configuration example of an information processing apparatus according to the embodiment; 10 is a diagram illustrating an example of a user information table stored in a user information storage unit according to the embodiment; FIG. 11 is a diagram illustrating an example of a transaction target information table stored in a transaction target information storage unit according to the embodiment; FIG. 12 is a diagram illustrating an example of a content table stored in a content storage unit according to the embodiment; FIG. FIG. 13 is a flowchart illustrating an example of the flow of information processing executed by the information processing device. FIG. 14 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing apparatus according to the embodiment.

Hereinafter, modes for implementing an information processing apparatus, an information processing method, and an information processing program (hereinafter referred to as "embodiments") according to the present application will be described in detail with reference to the drawings. The information processing apparatus, information processing method, and information processing program according to the present application are not limited to this embodiment. Further, each embodiment can be appropriately combined within a range that does not contradict the processing contents. Also, in each of the following embodiments, the same parts are denoted by the same reference numerals, and overlapping descriptions are omitted.

[1. Example of information processing]
[1.1. Overview of information processing]
First, an information processing method performed by the information processing apparatus according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram for explaining an information processing method performed by an information processing apparatus according to an embodiment.

The information processing device 1 executes information processing related to transaction objects traded in electronic commerce (EC) services. Here, the transaction object is the use of goods or services. Transaction objects traded in electronic commerce services include transaction objects traded in various forms such as electronic shopping malls (online malls), online shopping sites, auction sites, and flea market sites. FIG. 1 shows an example of information processing for a transaction target traded on an electronic shopping mall.

FIG. 1 illustrates an example in which the information processing device 1 functions as a search device that provides search results to a terminal device 2 used by a user U who inputs a search query. A search query is entered by entering a search keyword, and a search query including the search keyword is transmitted from the terminal device 2 to the information processing device 1 . Note that the input of the search query is not limited to input using the keyboard displayed on the terminal device 2 , and may be input by designating keyword candidates displayed on the terminal device 2 . In the following description, the search query includes a search keyword, but may include a search image instead of or in addition to the search keyword. Although only one terminal device 2 is illustrated in FIG. It is provided to each of the terminal devices 2 .

The information processing device 1 has a function as a search engine that executes a search process for, for example, a transaction target traded on an electronic shopping mall. For example, the information processing device 1 has a database (also referred to as a "transaction target database") in which transaction targets to be searched using a search query are indexed and stored, and the information in the transaction target database is Execute search processing as a target. For example, the information of the transaction target database is stored in the storage unit 11 (see FIG. 9).

As described above, FIG. 1 illustrates a case in which the information processing device 1 also functions as a search device, that is, the information processing device 1 and the search device are integrated. There may be. In this case, the information processing system 100 (see FIG. 9) includes a search device that performs search processing for a search query and provides a search service that provides search results. For example, the information processing device 1 transmits a search query including a search keyword input by the user U to the search device, receives search results from the search device, and sends content generated using the search results to the user U. Send to the terminal device 2 .

For example, when the external device 3 is a search device, the information processing device 1 transmits a search query including a search keyword input by the user U to the external device 3, receives search results from the external device 3, and performs the search. The content generated using the result is transmitted to the terminal device 2 used by the user U. The above system configuration is merely an example, and the information processing system 100 may have any device configuration as long as desired information can be provided to the user U who has entered a search keyword.

First, the information processing device 1 acquires user information, which is information about the user U, from the external device 3 (step S1). The external device 3 transmits user information to the information processing device 1 . For example, the external device 3 transmits user information including user U attribute information to the information processing device 1 . The attribute information includes, for example, information indicating demographic attributes such as age, gender, area of residence, and occupation, and information indicating psychographic attributes such as interests and lifestyles. Note that the attribute information is merely an example, and the user information may include any information as long as it relates to the user U. For example, the user information may include a history of actions of the user U, such as actions on the Internet. For example, the user information may include an action history of the user U, such as a viewing history of information about a transaction target in an electronic commercial transaction service such as an electronic shopping mall, and a purchase history.

Further, the information processing device 1 acquires information (also referred to as "transaction target information") regarding the transaction target from the external device 3 (step S2). For example, the information processing device 1 acquires, from the external device 3, transaction target information of a transaction target to be traded on the electronic shopping mall. For example, the information processing device 1 acquires, from the external device 3, transaction target information of a transaction target that is being sold (exhibited) on the electronic shopping mall. For example, the information processing device 1 acquires the transaction target information of the transaction target from the external device 3 every predetermined period.

Here, the transaction target information includes various information regarding the transaction target. For example, the transaction target information includes various information used to provide information on the transaction target. For example, the transaction target information includes the seller, sale price, image of the transaction target, category, title, description, payment method, sales period, number of sales, and the like.

Further, for example, the transaction target information includes the sales history of the transaction target. For example, the transaction target information includes information such as the number of reservations, the number of orders, the number of sales, and the order amount (sales) of the transaction target. In addition, for example, the transaction target information includes the period elapsed from the date and time when the transaction target was sold, the number of views of information indicating the transaction target (number of PV (page views)), the number of reviews of the transaction target, the evaluation of the transaction target, etc. information is included.

The transaction target information may also include information on external factors related to the transaction target. For example, transaction target information may include information posted on a transaction target on an SNS (Social Networking Service). For example, the transaction target information may include posted information posted by the user U on the SNS. For example, the posted information includes text information posted by the user U on Twitter (registered trademark), Facebook (registered trademark), Instagram (registered trademark), YouTube (registered trademark), etc., or image information such as images and videos. may be For example, the trading object information may include information on the number of searches for the trading object.

Further, the transaction target information may include information on internal factors related to the transaction target. For example, the transaction target information may include incentive information for the transaction target. For example, the transaction target information may include information on incentives given to the user U for the transaction target. For example, the transaction target information may include coupon information for the transaction target. For example, the deal information may include price reduction information about the deal. Note that the above is only an example, and the transaction target information may include any information as long as it is information related to the transaction target.

In the above example, the information processing device 1 performs the acquisition process in steps S1 and S2. information may be obtained at the same time.

The information processing device 1 also receives a search query input by the user U from the terminal device 2 (step S3). For example, when the user U inputs a search keyword, the terminal device 2 transmits a search query including the search keyword input by the user U to the information processing device 1 . Thereby, the information processing device 1 acquires the search query input by the user U. FIG.

For example, when the user U inputs "sneakers" as a search keyword into the terminal device 2, the terminal device 2 transmits a search query including the search keyword "sneakers" input by the user U to the information processing device 1. . For example, the user U enters "sneakers" as a search keyword in an input field such as a search box BX1 in FIG. 2, which will be described later.

Then, the information processing device 1 that has received the search query generates search results corresponding to the search query through search processing using the search query as processing for providing information to the user U (step S4). For example, when the search keyword included in the search query is "sneakers," the information processing apparatus 1 performs search processing using "sneakers" to generate search results corresponding to "sneakers." Hereinafter, a search query including "sneakers" as a search keyword may be referred to as a search query "sneakers."

The information processing device 1 extracts transaction target information corresponding to the search query "sneakers" using information in the transaction target database. For example, the information processing device 1 performs search processing using the search query "sneakers" on the transaction target database, and extracts transaction target information corresponding to the search query "sneakers". For example, the information processing device 1 extracts transaction target information including information such as a character string corresponding to the search query "sneakers" in the transaction target information.

It should be noted that the extraction of information corresponding to the search query is performed using a conventional technique, and any processing may be used as long as the transaction target can be specified, and detailed description thereof will be omitted. When a device (search device) other than the information processing device 1 performs the search process, the information processing device 1 transmits a search query to the search device and receives search results of the search query from the search device. The information processing device 1 generates search results for the search query using the transaction target information extracted as information corresponding to the search query. For example, the information processing device 1 generates list information of transaction targets of the extracted transaction target information as a search result for the search query.

The information processing device 1 also estimates the intent of the search query received in step S3 (hereinafter, may be referred to as query intent) (step S5). The information processing device 1, for example, estimates the degree of relevance of the search query to each of the plurality of intention candidates as the intention of the search query. The intention candidate is, for example, the category of the transaction object, the brand of the transaction object, the usage scene of the transaction object, the specifications of the transaction object, the price range of the transaction object, or the usage layer of the transaction object.

Based on the search query, the information processing apparatus 1 calculates a score indicating the degree of relevance of the search query to each of the plurality of intention candidates, and treats these scores or information according to the score as the intention of the search query. In the following, an example in which a plurality of intention candidates are transaction target categories and transaction target brands will be mainly described, but intention candidates are not limited to categories or brands.

For example, the information processing apparatus 1 uses an estimation model for each intention candidate to calculate a score indicating the degree of relevance of the search query to each intention candidate, and estimates these scores or information according to the score as the query intention. can be done. The estimation model is a trained model generated by machine learning, receives a search keyword included in a search query, and outputs a score indicating the degree of relevance of the search query to the intention candidate.

An estimation model for each intention candidate is generated by machine learning using a neural network such as a convolutional neural network or a recurrent neural network, but is not limited to such examples. For example, an estimated model for each intention candidate may be generated using machine learning using a learning algorithm such as linear regression or logistic regression instead of using a neural network. Further, instead of an estimation model for each intention candidate, the information processing apparatus 1 inputs a search keyword included in a search query, and outputs a single estimation model that outputs a score indicating the degree of relevance of the search query to a plurality of intention candidates. can also be used.

Next, the information processing device 1 generates a plurality of pieces of ranking information according to the query intent estimated in step S5 (step S6). For example, the information processing device 1 determines the type and order of a plurality of ranking information to be provided to the user U based on the query intent, and provides the user U with the determined type and order of the ranking information. Generate multiple ranking information for The classification of the ranking information is defined, for example, by the ranking cutoff and the display format of the ranking information.

The point of view of the ranking is the point of view (point of view) for the transaction target. For example, perspectives (perspectives) on the transaction target include categories, brands, usage scenes, specifications (product specifications and functions) to which the transaction target applies, problems that can be solved by the transaction target, and people who can be solved by the transaction target. It includes various perspectives (viewpoints) corresponding to the transaction target, such as worries.

The display format of the ranking information is the arrangement format of the plurality of ranking elements that constitute the ranking information, and includes a list format (an example of the first display format), a grid format (an example of the second display format), and the like. The list format is a display format in which a plurality of ranking elements that make up the ranking information are arranged in a row, and the grid format is a display format in which a plurality of ranking elements are arranged in a matrix of n rows x m columns. . n and m are integers of 2 or more.

The display format of the ranking information may include a display format in which a plurality of ranking elements are arranged in one line instead of or in addition to the second display format. Also, the display format of the ranking information may be divided into a display format including the photograph of the transaction object and a display format not including the photograph of the transaction object.

The information processing device 1, for example, determines the types of a plurality of ranking information to be provided to the user U based on the query intention, and sets a plurality of ranking information with higher priority to rank information having a higher relevance to the query intention. is determined as the order of the ranking information.

For example, the information processing device 1, the score indicating the degree of relevance of the search query to the category further classified category specified by the search query and the relevance of the search query to the brand to be traded in the category specified by the search query Based on this, the types and order of the plurality of ranking information are determined.

The information processing device 1 generates a plurality of ranking information based on the determined types of the plurality of ranking information (for example, cuts and display formats; hereinafter sometimes referred to as determination types) and the order of arrangement of the plurality of ranking information. do.

The information processing device 1 extracts or determines transaction objects that are ranking elements from the perspective indicated by the determined type, and performs a process of calculating a score for each extracted or determined transaction object for each determined type. The information processing device 1 uses the calculated score to rank the trading objects for each determination type, and generates ranking information in which a plurality of ranking elements are arranged in a display format indicated by the determination type for each determination type. For example, the information processing device 1 generates ranking information by assigning ranks to ranking elements in descending order of score, and arranging a plurality of ranking elements up to a preset rank from the highest rank.

For example, the information processing device 1 calculates a score of a transaction target extracted or determined as a ranking element based on the transaction target information related to the transaction target for each determination type. The information processing apparatus 1 receives transaction target information of a transaction target, and calculates a score of the transaction target using a score function, which is a function that outputs the score of the transaction target. The information processing device 1 calculates the score of the transaction target using a score function in which the output value increases as multiple factors such as the number of orders of the transaction target, the order amount, and the number of views (number of PVs) increase. may

For example, the information processing device 1 multiplies each of a plurality of elements such as the number of orders to be traded, the amount of orders, and the number of views (number of PVs) by the weight (coefficient) of each element (hereinafter referred to as "partial score"). ) is used to calculate the score of the transaction target. The information processing device 1 may calculate the sum of the partial scores of each of the multiple elements of the transaction object as the score of the transaction object. The information processing device 1 may calculate the average of the partial scores of each of the multiple elements of the transaction target as the score of the transaction target.

In addition, the information processing device 1 calculates the score of the transaction target using aggregated values aggregated for each predetermined period for each of a plurality of elements such as the number of orders, the order amount, and the number of views (number of PVs) of the transaction target. You may The information processing device 1 may calculate the score of the transaction target using the aggregated value aggregated for each date for each of the multiple elements of the transaction target. In this case, the information processing device 1 calculates the score of the transaction object using the period score obtained by multiplying the weight (coefficient) of each element by the total value of the plurality of elements of the transaction object aggregated for each predetermined period. can be calculated. The information processing device 1 may calculate the score of the transaction target using a value obtained by multiplying the period score of each period by an attenuation coefficient according to the difference between the period and the calculation time. The information processing apparatus 1 may calculate the score of the transaction target using a value obtained by multiplying the period score of each period by an attenuation coefficient that decreases in value as the period progresses in the past.

Note that the above is only an example, and the information processing device 1 may use any information to calculate the score of the transaction target. The information processing apparatus 1 may correct the score of the transaction object using a coefficient based on the evaluation of the sales source (store) that sells the transaction object to the calculated score of the transaction object. The information processing device 1 may correct the score of the transaction object by multiplying the calculated score of the transaction object by a coefficient based on the evaluation of the seller who sells the transaction object.

Then, the information processing device 1 generates content to be provided to the user U using the search result corresponding to the search query and a plurality of pieces of ranking information corresponding to the search query (step S7). The information processing device 1 displays a first tab for displaying search results corresponding to the search query and ranking information corresponding to the search query, in addition to search results corresponding to the search query and ranking information corresponding to the search query. and a second tab for displaying the content.

Then, the information processing device 1 transmits the content including the search result corresponding to the search query and the ranking information corresponding to the search query to the terminal device 2 of the user U who entered the search keyword included in the search query ( step S8). When there is a plurality of ranking information corresponding to the search query, the information processing device 1 transmits a part of the plurality of ranking information to the terminal device 2, and then transmits the remaining ranking information according to the display state of the ranking information on the terminal device 2. Although part or all of the information is transmitted to the terminal device 2, it is not limited to such an example. For example, the information processing device 1 can transmit a plurality of pieces of ranking information to the terminal device 2 at once.

An example of content provided to the terminal device 2 will now be described with reference to FIG. FIG. 2 is a diagram showing an example of content provided to the user U from the information processing device 1 according to the embodiment. In FIG. 2, content C provided from information processing device 1 to terminal device 2 and displayed on terminal device 2 includes search box BX1, first tab TB1, second tab TB2, and second tab content C2. . The search box BX1 shown in FIG. 2 shows "sneakers" as a search keyword input by the user U. As shown in FIG. The content C also includes a first tab content C1 (see FIG. 7), which will be described later.

A first tab TB1 shown in FIG. 2 is a GUI (Graphical User Interface) selected to display a first tab content C1 (see FIG. 7) described later on the terminal device 2, and the second tab shown in FIG. TB2 is a GUI selected to cause the terminal device 2 to display the second tab content C2. The first tab content C1 and the second tab content C2 are selectively displayed in the content C display area AR by operating the first tab TB1 or the second tab TB2.

In FIG. 2, when the first tab TB1 is selected by the user U, the first tab content C1 in which the search results of the search query "sneakers" are arranged is displayed in the display area AR. Further, when the user U selects the second tab TB2, the display area AR displays the second tab content C2 in which a plurality of ranking information items of the transaction target corresponding to the search query "sneakers" are arranged.

FIG. 2 shows a state in which the second tab TB2 is selected, and the terminal device 2 displays the second tab content C2 in which the ranking information of the transaction target corresponding to the search query "sneakers" is arranged in the display area AR. Specifically, in the display area AR of the content C, ranking information in which a plurality of trading objects corresponding to the search query "sneakers" are arranged in descending order of score as ranking elements is displayed as the second tab content C2. be.

In this way, the information processing device 1 switches between the first tab content C1 and the second tab content C2 according to the user U's selection, and displays the content in the display area AR on the terminal device 2 of the user U. provide. As a result, if the user U using the terminal device 2 receiving the provision of the content C wants to simply view the search results, the user U displays the first tab content C1 on the terminal device 2 and wants to view the ranking information. causes the terminal device 2 to display the second tab content C2. In the case of the content C displayed by switching the information in this way, the user U using the terminal device 2 switches the display according to the content he/she wants to see, and causes the terminal device 2 to display the content, and confirms the content. can do. Therefore, the information processing device 1 can provide appropriate content.

[1.2. Details of various processes]
Assuming the information processing described above, details of various processes will be described. It should be noted that descriptions of the same points as those described above will be omitted as appropriate. Also, various types of processing described below may be combined as appropriate. Note that the above is merely an example, and various types of processing may be combined in any way within the range in which combined processing is possible.

[1.2.1. Estimation of query intent]
The information processing device 1 can also estimate the query intention by dividing the score indicating the degree of relevance of the search query to each of the plurality of intention candidates into a plurality of ranges. For example, the information processing device 1 divides the score indicating the degree of relevance of the search query to each category and brand into M stages. M is an integer of 2 or more.

FIG. 3 is a diagram showing an example of classification of scores indicating the degree of relevance of a search query to each of categories and brands in the information processing apparatus 1 according to the embodiment. In the example shown in FIG. 3, the information processing device 1 classifies the score indicating the degree of relevance of the search query to each of the categories and brands into three stages of a first range, a second range, and a third range.

The first range is a range in which the score indicating the degree of association is less than the first threshold Th1, and indicates, for example, that there is no degree of association with the intention candidate. The second range is a range from the first threshold value Th1 to the second threshold value Th2 (>Th1), which indicates the degree of relevance. Indicates that it cannot be cut. The third range is a range in which the score indicating the degree of association exceeds the second threshold Th2, and indicates, for example, that intention candidates are narrowed down to one specific range. The first threshold Th1 and the second threshold Th2 are, for example, Th1=0.1 and Th2=0.9, but are not limited to such examples. Also, the first threshold Th1 and the second threshold Th2 may be set for each intention candidate.

In the example shown in FIG. 3, the first range is represented as "no intention", the second range is represented as "N strong", and the third range is represented as "1 strong". There is Further, hereinafter, the degree of relevance of search queries to brands may be referred to as category intent, and the degree of relevance of search queries to brands may be referred to as brand intent.

Categories are defined, for example, in a tree-like hierarchy. In this case, the category intent is more than 1 if it can be narrowed down to one of the lowest category categories in the tree-like hierarchy. Also, the category intention is N-strong when two or more lowest-level categories (or a predetermined number of lowest-level categories) can be narrowed down from a plurality of lowest-level categories. Such two or more lowest-layer categories are, for example, the lowest-layer categories in which the upper-layer categories in the tree-like hierarchy are the same or similar, but are not limited to such examples. In addition, the category intention is not intended when one of two or more lowest-layer categories cannot be narrowed down from a plurality of lowest-layer categories that have the same category immediately above the tree-like hierarchy. . It should be noted that the case where the category intention is no intention may be, for example, the case where the category specified by the search query is a category higher than the lowest layer.

For example, the category "sneakers" is an intermediate category obtained by classifying the large category "shoes". The category "sneakers" of the middle category is divided into small categories such as "ladies' sneakers", "men's sneakers", "ladies' slip-ons", and "children's sneakers". Women's slip-ons are not sneakers, but they are traded items that are frequently searched for or selected together with sneakers. The sub-category that classifies the category "sneakers" is an example of the lowest category in the tree-like hierarchy.

In this case, the category intention is "strong 1" if it can be narrowed down to one small category (lowest level category), and two or more categories out of multiple small classification categories (lowest level category) If it can be narrowed down to (less than 4 categories), it is "N strong". In addition, when it is not possible to narrow down to two or more categories (less than four categories) among the categories of the plurality of small classifications (bottom level categories), the category intention is "no intention".

In addition, the brand intention is "no intention" if, for example, it cannot be narrowed down to two or more brands out of a plurality of brands (or a specified number of brands or less), and two or more brands out of a plurality of brands ( or brands with a specified number or less), it is "N strong", and if it can be narrowed down to one small brand, it is "1 strong".

Note that the classification of the score indicating the degree of relevance of the search query to each of the plurality of intention candidates may be divided into two stages instead of three stages, or may be four stages or more. A result of classifying the scores indicating the degree of relevance of the search query with respect to each of the plurality of intention candidates is an example of information indicating the degree of relevance of the search query with respect to each of the plurality of intention candidates. The information processing apparatus 1 can also use scores indicating the degree of relevance of a search query to each of a plurality of intention candidates without classifying them. In this case, the score indicating the degree of relevance of the search query to each of the plurality of intention candidates is an example of information indicating the degree of relevance of the search query to each of the plurality of intention candidates.

[1.2.2. Determination of ranking information type and display format]
The information processing device 1 determines the types and order of the plurality of ranking information to be provided to the user U based on the query intent. The type of ranking information is defined by, for example, the ranking aspect and the display format of the ranking information, but is not limited to such an example.

The information processing device 1 can also determine the types and order of the plurality of ranking information to be provided to the user U using the search keywords included in the search query in addition to the query intent. In this case, the information processing device 1 determines whether or not the search keyword included in the search query designates one lowest-level category. The information processing device 1 also determines whether or not the search keyword included in the search query designates one brand.

In the following, when the search keyword specifies one lowest category, it may be described as "category specified", otherwise, it may be described as "category not specified". When the search keyword designates one brand, it may be described as "with brand designation", otherwise, it may be described as "without brand designation". Also, the determination result of whether or not the category and brand are designated may be described as "query designation determination result".

The information processing device 1 determines the types and order of the plurality of ranking information to be provided to the user U based on the query intent and the query designation determination result. For example, the information processing device 1 determines the types and order of the plurality of ranking information to be provided to the user U based on the combination of the query designation determination result, category intention, and brand intention.

The information processing device 1 has case classification determination information that associates the types and order of a plurality of ranking information to be provided to the user U for each combination of category designation, category intention, brand designation, and brand intention. . The information processing apparatus 1 determines which case of the case classification determination information corresponds to the combination of the determined category designation, category intention, brand designation, and brand intention.

FIG. 4 is a diagram showing an example of case classification determination information possessed by the information processing apparatus 1 according to the embodiment. In the case classification determination information shown in FIG. 4, for example, case K1 is applied when there is category designation or the category intention is more than 1, and there is no brand designation and brand intention is no intention. In addition, case K2 is applied when there is a category designation or the category intention is more than 1, and there is no brand designation and the brand intention is more than N. In addition, case K3 is applied when there is a category specification or the category intention is more than 1 and there is a brand specification or the brand intention is more than 1.

Case K4 is applied when there is no category designation and the category intention is N-strong and there is no brand designation and the brand intention is no intention. Case K5 is applied when there is no category specification and the category intention is N-strong and there is no brand specification and the brand intention is N-strong. Case K6 is applied when there is no category designation and the category intention is N-strong and there is brand designation or the brand intention is a little over 1.

In addition, case K7 is applied when there is no category designation and the category intention is no intention and there is no brand designation and the brand intention is no intention. Case K8 is applied when there is no category designation and the category intention is no intention and there is no brand designation and the brand intention is N-strong. Case K9 is applied when there is no category designation and the category intention is no intention and there is brand designation or the brand intention is more than 1.

The information processing device 1 determines the type and order of the plurality of ranking information to be provided to the user U based on the information of the type and order of the plurality of ranking information associated with the determined case. The information processing device 1, for example, has ranking information determination information in which information on the type and order of ranking information is defined for each case, and provides the information to the user U using this ranking information determination information. Determine the types and order of the plurality of ranking information to be displayed.

FIG. 5 is a diagram showing an example of ranking information determination information possessed by the information processing apparatus 1 according to the embodiment. In the ranking information determination information shown in FIG. 5, the types and order of ranking information are defined for each of cases K1 to K9.

In case K1, "single category synthesis" and "list format" are defined as the types of the first ranking information (ranking cut and display format), and "query synthesis" and "grid display" are defined as the second ranking information types. format is specified. "1 category overall" indicates that the ranking cut is a cut that ranks a plurality of transaction objects included in one lowest category specified by category designation or category intention without brand limitation. "Query comprehensive" indicates that it is a way of ranking multiple transaction targets narrowed down by a search using a search keyword included in the search query without limitation of category and brand.

When the case determined from the query intention and the query specification determination result is case K1, the information processing device 1 determines the type of the first ranking information to be "1 category comprehensive" and "list format", and determines the second ranking. Determine the type of information as "query synthesis" and "grid format".

In case K2, "single category general" and "grid format" are defined as the types of the first ranking information (ranking cut and display format), and "multi-brand" and "grid format" are defined as the types of the second ranking information. Format" is defined, and "query synthesis" and "grid format" are defined as the types of the third ranking information. "Multi-brand" indicates that it is a group of cuts including multiple cuts that rank by brand a plurality of transaction targets included in one lowest category specified by category designation or category intention.

Here, it is assumed that the case determined from the query intent and query specification determination result is case K2 and the brands are brands #1 to #3. In this case, the information processing device 1 determines the type of the first ranking information to be "1 category general" and "grid format", and sets the types of the second to fourth ranking information to "1 category brand #1 and "grid format", "1 category brand #2" and "grid format", and "1 category brand #3" and "grid format".

The alignment positions of "1 category brand #1", "1 category brand #2", and "1 category brand #3" are positioned so that the highest score of the corresponding brand is prioritized. Note that the brand score is calculated by, for example, the same calculation method as the transaction target score. For example, the brand score is the sum of the scores of the transaction targets included in the brand among the multiple transaction targets included in the non-lowest level category (e.g., category "sneakers") specified by the search keyword, or the search keyword It may be the sum of the scores of a preset number of transaction targets that are included in the brand and have the highest scores among the plurality of transaction targets that are included in the non-lowest level category specified by . "1 category brand #P" (P is any of 1 to 3) is a trading target of brand #P among multiple trading targets included in one lowest category specified by category designation or category intention It shows that it is a cut to rank.

Similarly, when the case determined from the query intent and query specification determination result is one of cases K3 to K9, the information processing device 1 determines the type and arrangement order of the ranking information corresponding to the determined case. Use the table for

Note that in FIG. 5, "1 category & 1 brand overall" is the one specified by brand designation or brand intention among a plurality of transaction targets included in one lowest-level category that is specified by category designation or category intention. It shows that it is a cut that ranks multiple transaction targets of one brand. In addition, "one brand comprehensive" is one brand specified by brand designation or brand intention among multiple transaction objects included in a non-lowest level category (for example, category "sneakers") specified by category designation. It shows that it is a cut that ranks the trading objects of

In addition, "multi-brand (one category)" is, for example, a category that satisfies a specific condition (for example, the category with the highest score) or is randomly selected from a plurality of categories specified by category designation or category intention. It is a group of cuts that includes multiple cuts that rank the transaction targets of the category that has been identified by brand. The category score is calculated, for example, by the same calculation method as the transaction target score. For example, the category score is the average of the scores of the multiple transaction targets included in the target category, or the score of the highest preset number of the multiple transaction targets included in the target category. may be the total value of

Also, "multi-category" indicates, for example, a group of cuts including a plurality of cuts for ranking multiple transaction targets for each category below the category specified by the search keyword. For example, if the category specified by the search keyword is "sneakers", the lower multiple categories are women's sneakers, men's sneakers, women's slip-ons, children's sneakers, and the like.

"One brand overall" is a cut that ranks multiple transaction targets of one brand specified by brand designation or brand intention among multiple transaction targets included in the category specified by the search keyword. show.

It should be noted that the method of determining the type and order of the ranking information by the information processing device 1 is not limited to the example described above. For example, the ranking information determination information is not limited to the example shown in FIG. Also, the ranking information determination information may be provided for each attribute of the user U. FIG. In this case, the information processing device 1 uses ranking information determination information corresponding to the attribute of the user U who has input the search query to determine the type and order of the ranking information.

Moreover, the above is merely an example, and the information processing device 1 may determine the type and order of ranking information by appropriately using various information in addition to or instead of the query intent. The information processing device 1 may determine the approach according to the user attributes or the concerns of the user U, in addition to or instead of the query intention. Further, the information processing device 1 may determine the approach based on the search history. For example, the information processing device 1 may determine the approach based on the interest estimated from the search history in addition to or instead of the query intention.

Also, the ranking information determination information may be provided for each non-lowest level category. In this case, if the category specified by the search keyword included in the search query is a non-lowest level category, the information processing device 1 corresponds to the non-lowest level category specified by the search keyword included in the search query. The ranking information determination information is used to determine the type and arrangement order of the ranking information.

[1.2.3. Generation of ranking information in the determined cut]
The information processing device 1 ranks the plurality of trading objects according to each of the plurality of different perspectives determined as described above, and generates a plurality of ranking information. FIG. 6 is a diagram showing an example of ranking of transaction targets from multiple perspectives in the information processing apparatus 1 according to the embodiment. In addition, in FIG. 6, the state divided|segmented for every cut is shown for description.

Also, in FIG. 6, abstract character strings such as cut #1, cut #2, . . . are used. This is information indicating a specific approach such as "single category synthesis" and "query synthesis".

For example, the information processing device 1 selects a transaction target corresponding to each of cut #1, cut #2, . determine (identify) For example, the information processing device 1 determines a transaction target corresponding to each of cut #1, cut #2, .

The information processing device 1 ranks the transaction objects corresponding to the cut #1 as the ranking elements of the cut #1, thereby generating the ranking of the cut #1. For example, the information processing device 1 calculates a score for a transaction object corresponding to cut #1, which is a ranking factor, and generates a ranking of cut #1 using the calculated score. Note that the calculation of the score of the transaction target is the same as the content explained in FIG. 1, so the explanation is omitted. For example, the information processing device 1 generates the ranking information of the cut #1 by assigning a higher ranking in descending order of the score.

The list information TA1 shown in FIG. 6 is information in which the rank of the transaction object, the transaction object ID (Identifier), which is identification information for identifying the transaction object, and the score are associated with each other. In the list information TA1, the trading object with the rank “1st” is the trading object with the trading object ID “M34” and the score is “0.94”. The transaction object with the rank “2nd” is the transaction object with the transaction object ID “M7” and the score is “0.89”. The transaction object with the rank “3rd” is the transaction object with the transaction object ID “M1” and the score is “0.85”. The information processing device 1 generates the ranking information of the cut #1 using the information shown in the list information TA1.

Similarly, the information processing device 1 ranks the transaction objects corresponding to the cut #2 as the ranking elements of the cut #2, thereby generating the ranking of the cut #2. For example, the information processing device 1 calculates a score for a transaction object corresponding to cut #2, which is a ranking factor, and generates ranking information for cut #2 using the calculated score.

The list information TA2 shown in FIG. 6 is information in which the rank of the transaction object, the transaction object ID, which is identification information for identifying the transaction object, and the score are associated with each other. In the list information TA2, the trading object with the rank “1st” is the trading object with the trading object ID “M162” and the score is “0.84”. The transaction object with the rank “2nd” is the transaction object with the transaction object ID “M78” and the score is “0.74”. The transaction object with the rank “3rd” is the transaction object with the transaction object ID “M18” and the score is “0.73”. The information processing device 1 generates the ranking information of the cut #2 using the information shown in the list information TA2.

When there are cuts other than cuts #1 and #2, the information processing device 1 generates ranking information for cuts other than cuts #1 and #2 by similar processing.

[1.2.4. Example of content including search results and multiple ranking information]
Next, an example of search results (first tab content) and ranking information (second tab content) included in content C provided from the information processing device 1 to the terminal device 2 will be described with reference to FIG. FIG. 7 is a diagram showing an example of search results and ranking information provided by the information processing device 1 according to the embodiment.

FIG. 7 shows first tab content C1 and second tab content C2 included in content C generated by information processing device 1 when search query "sneakers" is transmitted from terminal device 2 to information processing device 1. ing. The user U can display the first tab content C1 in the display area AR by selecting the first tab TB1 (see FIG. 2), and by selecting the second tab TB2 (see FIG. 2) , the second tab content C2 can be displayed in the display area AR. Either the information processing device 1 or the terminal device 2 may switch between the first tab content C1 and the second tab content C2.

The information processing device 1 determines that the number of lower-layer categories included in the category of the search query "sneakers" acquired from the terminal device 2 is equal to or less than a specified number, and that the number of brands to be traded in the category "sneakers" is equal to or less than a specified number. Therefore, it is determined that each of the category intention and the brand intention is N-strong. Further, since the search query is "sneakers", the information processing apparatus 1 determines that both category designation and brand designation are not specified. In this case, the information processing apparatus 1 determines that case K5 applies if the case classification determination information is in the state shown in FIG.

In this case, if the ranking information determination information is in the state shown in FIG. )” and the ranking information whose cut type is “general query” are generated as the second tab contents C2. A plurality of pieces of ranking information whose cut type is "multi-category" include ranking information of women's sneakers, ranking information of men's sneakers, ranking information of ladies' slip-ons, ranking information of children's sneakers, and the like. Each of the plurality of ranking information whose cut type is "multi-brand (one category)" is ranking information in which a plurality of trading objects classified into one category of sneakers (for example, ladies' sneakers) are ranked by brand. be. The "general query" cut type is ranking information in which transaction targets searched for "sneakers" are ranked as ranking factors.

In the second tab content C2 shown in FIG. 7, ranking information for ladies' sneakers, ranking information for men's sneakers, and the like are arranged in order. Each of the ranking information shown in FIG. 7 shows the top six trading objects, and if the user U wants to know the ranking of the trading objects ranked seventh and below, the character string " By selecting "See more", the second tab content C2 including the ranking information including the ranking of the transaction objects ranked 7th or lower is transmitted from the information processing device 1 to the terminal device 2, and the terminal device 2 A second tab content C2 containing ranking information including ranking of trading objects is displayed.

FIG. 8 is a diagram showing another example of ranking information provided by the information processing device 1 according to the embodiment. In the example shown in FIG. 8, when the search keyword included in the search query acquired from the terminal device 2 is "sneaker ladies", the second tab included in the content C transmitted from the information processing device 1 to the terminal device 2 It is a figure which shows an example of the content C2. When the search keyword included in the search query is "sneaker ladies", the information processing device 1 determines that there is category designation or that the category intention is more than 1, and that there is no brand designation and no brand intention. It is determined that The information processing apparatus 1 determines that case K1 applies if the case classification determination information is in the state shown in FIG.

In this case, if the ranking information determination information is in the state shown in FIG. and the second tab content C2 including information. The ranking information whose cut type is "one category comprehensive" is the ranking information of women's sneakers. The ranking information whose cut type is "general query" is ranking information obtained by ranking transaction targets searched for with the search keyword "sneakers ladies" as a ranking factor.

The display format of the ranking information whose cut type is "one category comprehensive" is a list format, and in the example shown in FIG. there is Compared to the grid format, the list format adds more information about the ranking target, the amount of information is large, and the number of ranking elements included is also large.

For example, compared to the grid format, the list format includes brand names, product names, colors, materials, and other information (e.g., user evaluation values, user evaluation numbers, etc.), and has a large amount of information. . Therefore, the user U can more clearly grasp what the ranking elements are based on the ranking element information included in the ranking information. Also, since the list format contains more ranking elements than the grid format, it is possible to select "more" less often to get the ranking information starting from the next position. The convenience of the person U can be enhanced. It should be noted that even in the grid format, brand names and product names may be included.

In the example shown in FIG. 5, the list format is set for highly specific query intent, such as when there is an intent candidate specified or when there is more than one intent candidate, otherwise the grid format is set. Therefore, the user U can check the ranking information in the list format by making the search keyword concrete, or check the ranking information in the grid format by making the search keyword abstract. can do. In the example shown in FIG. 5, the second and subsequent ranking information among the plurality of ranking information are in the grid format, but some of the ranking information can also be determined in the list format based on the query intent. The display format of the ranking information whose cut type is "general query" is a grid format, and in the example shown in FIG. Although ranked and arranged, the number of ranking elements is not limited to six.

[1-3. summary〕
Through the various types of processing described above, the information processing apparatus 1 can propose a search method of selecting from content that can be the decisive factor of "ranking" at a conspicuous position in the first view by providing tabs related to ranking. Therefore, the information processing device 1 can reduce the possibility that the user U who cannot select the transaction target by himself/herself leaves. In addition, the information processing apparatus 1 can promote the migration of the user U (light user) whose use time is shorter than the predetermined time.

For example, a use case is assumed in which there are many options (transaction targets) corresponding to the search keyword input by the user U, the user U has little prior knowledge, and is at a loss as to what to buy. In this case, for example, the information processing device 1 provides rankings for each aspect related to categories, specifications, and the like. This makes it easy for the user U to grasp the outline of the transaction target even with a search query with a broad intention, deepen the intention, and naturally narrow down the options.

In addition, the information processing device 1 can provide content in which the transaction objects are arranged according to each aspect, thereby making it easier for the user U to embody the image of purchasing the transaction objects. In addition, the information processing apparatus 1 can provide content arranged according to brands or the like, thereby making it easier for the user U to narrow down options based on trends, popularity, and the like.

In addition, the information processing device 1 can provide the user U with a plurality of pieces of ranking information that are arranged in different order determined based on the query intention. Thereby, the information processing device 1 can more appropriately provide a plurality of ranking information corresponding to the search query. For example, the information processing device 1 provides a plurality of ranking information in an order such that ranking information having a higher degree of relevance to a query intent is prioritized, thereby more appropriately providing a plurality of ranking information. can be done.

In addition, the information processing device 1 can provide ranking information in a display format determined based on the intent of the query. Thereby, the information processing device 1 can more appropriately provide the ranking information corresponding to the search query. For example, the information processing device 1 provides the ranking information in a list format when the query intention is highly specific, and provides the ranking information in a grid format otherwise. Thereby, the information processing device 1 can more appropriately provide the ranking information corresponding to the search query.

Hereinafter, the configuration of the information processing system 100 including the information processing device 1, the terminal device 2, and the external device 3 that perform such processing will be described in detail.

[2. Configuration of Information Processing System 100]
As shown in FIG. 9, the information processing system 100 includes an information processing device 1, a terminal device 2, and an external device 3. FIG. The information processing device 1, the terminal device 2, and the external device 3 are connected via a network N so as to be communicable by wire or wirelessly. The information processing system 100 shown in FIG. 9 may include a plurality of information processing apparatuses 1, a plurality of terminal devices 2, and a plurality of external devices 3. FIG.

The terminal device 2 according to the embodiment is an information processing device used by a user who accesses content such as web pages displayed on a browser and content for applications. For example, the terminal device 2 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 2 is not limited to the above example, and may be, for example, a smart watch or a wearable device.

The external device 3 according to the embodiment is an information processing device that provides various types of information, and is realized by, for example, a server device or a cloud system. For example, the external device 3 provides user information as various information. As another example, the external device 3 may be an Internet shopping site, an electronic shopping mall, a flea market site, an auction site, a travel or restaurant reservation site, a credit card contract site, or a financial product providing site. Provide information about the transaction object in such as.

The information processing device 1 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 information processing device 1 is communicably connected to various other devices via a network N. As shown in FIG.

[3. Configuration of Information Processing Device 1]
An example of the functional configuration of the information processing apparatus 1 will be described below. FIG. 9 is a diagram showing a configuration example of the information processing device 1 according to the embodiment. As shown in FIG. 9 , the information processing device 1 has a communication section 10 , a storage section 11 and a processing section 12 .

[3.1. communication unit 10]
The communication unit 10 is implemented by, for example, a NIC (Network Interface Card). The communication unit 10 is connected to the network N by wire or wirelessly, and transmits and receives information to and from other various devices. For example, the communication unit 10 transmits and receives information via the network N to and from each of the terminal device 2 and the external device 3 .

[3.2. Storage unit 11]
The storage unit 11 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 11 also has a user information storage unit 20 , a transaction target information storage unit 21 , a content storage unit 22 , and a ranking information storage unit 23 .

[3.2.1. User information storage unit 20]
The user information storage unit 20 stores various user information regarding the user U. FIG. FIG. 10 is a diagram showing an example of a user information table stored in the user information storage unit 20 according to the embodiment. In the example shown in FIG. 10, the user information table stored in the user information storage unit 20 has items such as "user ID" and "user information". For example, "user information" includes items such as "attribute information", "purchase history", and "browsing history".

“User ID” is an identifier that identifies the user U. The "attribute information" is attribute information about the attributes of the user U associated with the "user ID". For example, the attribute information includes user U's age, sex, phone number, address, and the like. In addition, the attribute information described above is only an example, and various information such as information indicating demographic attributes such as occupation other than age and gender, information indicating psychographic attributes such as interests and lifestyles, etc. may be included.

“Purchase history” is the purchase history of purchases made by the user U associated with the “user ID”. For example, the purchase history includes information on the transaction object purchased by the user U, the type of the 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 “browsing history” is a history of browsing of content by the user U associated with the “user ID”. For example, the “browsing history” may be a history of content displayed on the user U's terminal device 2 . For example, the viewing history includes information about content viewed (displayed) by the user U, the type of content, the number of times the content was displayed, and information about the date and time the content was displayed.

For example, in FIG. 10, "U1" identified by the user ID has attribute information "CH1", purchase history "PH1", and display history "WA1". In the example shown in FIG. 10, the attribute information and the like are represented by abstract codes such as "CH1", but the attribute information and the like are represented by specific numerical values, specific character strings, and various types of information. It may be a file format including

It should be noted that the user information storage unit 20 may store various types of information, not limited to the above, depending on the purpose. The user information storage unit 20 may store, for each user, information indicating the number of times the information has been displayed for each transaction object, for example, the number of times the user U has viewed the information.

[3.2.2. Transaction target information storage unit 21]
The transaction target information storage unit 21 stores various transaction target information related to the transaction target. FIG. 11 is a diagram showing an example of a transaction target information table stored in the transaction target information storage unit 21 according to the embodiment. In the example shown in FIG. 11, the transaction target information table stored in the transaction target information storage unit 21 includes "transaction target ID", "transaction target", "transaction target information", "category", and "product specific information". including items such as In FIG. 11, in order to explain the "category" and "product specific information", they are described as separate items from the "transaction target information", but the category information and product information are included in the transaction target information. may

Further, although illustration is omitted, the transaction object information storage unit 21 stores information indicating the cut to which each transaction object corresponds in addition to the category. For example, the transaction target information storage unit 21 stores information indicating a plurality of aspects such as the category, brand, usage scene, specifications, problems that can be solved by the transaction target, and human concerns that can be resolved by the transaction target to which each transaction target corresponds. is associated with each transaction object and stored.

"Transaction object ID" is an identifier for identifying a transaction object. "Transaction object" indicates the transaction object associated with the "transaction object ID". The “transaction object information” is the transaction object information of the transaction object identified by the “transaction object ID”.

"Category" is information on the category to which the transaction object identified by the "transaction object ID" corresponds. For example, when categories are defined in a hierarchical structure (for example, a tree-like hierarchical structure), the "category" is information on the lowest level category to which the transaction target applies, or information from the highest level category to the lowest level category to which the transaction target Contains information for each category up to the category. The “product identification information” is information related to the product to which the transaction object identified by the “transaction object ID” corresponds.

For example, in FIG. 11, "M1" identified by the trading object ID has trading object "MA1". In the example shown in FIG. 11, the transaction target is represented by an abstract code such as "MA1", but the transaction target is a specific character string (such as a product name) indicating the product sold by each seller. ).

Further, in FIG. 11, "M1" identified by the transaction object ID has transaction object information "MD1". In the example shown in FIG. 11, the transaction target information is represented by an abstract code such as "MD1", but the transaction target information may be in a file format or the like containing various information on the transaction target.

Also, in FIG. 11, "M1" identified by the transaction object ID has a category of "CT1". In the example shown in FIG. 11, the category is represented by an abstract code such as "CT1", but the category is information indicating the category of the transaction object.

Further, in FIG. 11, "M1" identified by the transaction object ID has "PD1" as the product identification information. In the example shown in FIG. 11, the product identification information is represented by an abstract code such as "PD1". It is information (for example, JAN code, etc.). Further, the product identification information is information indicating that there is no product corresponding to the transaction target when there is no product corresponding to the transaction target (for example, a flag indicating that there is no corresponding product).

Note that the transaction target information storage unit 21 may store various types of information, not limited to the above, depending on the purpose. For example, the transaction target information storage unit 21 may describe information such as the score and display count corresponding to each transaction target.

[3.2.3. Content storage unit 22]
The content storage unit 22 stores information about content. FIG. 12 is a diagram showing an example of a content table stored in the content storage unit 22 according to the embodiment. In the example shown in FIG. 12, the content table stored in the content storage unit 22 has items such as "content ID" and "content".

“Content ID” is an identifier for identifying content. "Content" is information about the content associated with the "content ID". Specifically, the content may indicate information about the contents of the content.

For example, the content is content related to a portal site. 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.

Also, 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 site, and the like.

For example, in FIG. 12, "C1" identified by the content ID has content "CO1". Note that in the example shown in FIG. 12, the content and the like are represented by abstract codes such as "CO1", but the content and the like are represented by specific numerical values, specific character strings, and files containing various information. It may be a format or the like.

Note that the content storage unit 22 may store various types of information, not limited to the above, depending on the purpose.

[3.2.4. Ranking information storage unit 23]
The ranking information storage unit 23 stores various kinds of information used to generate rankings. For example, the ranking information storage unit 23 stores case classification determination information shown in FIG. 4, ranking information determination information shown in FIG. Stores score calculation information and the like used to calculate various scores of a target.

The ranking reference information is, for example, information indicating a ranking reference for ranking in order from the highest score. The score calculation information is, for example, information such as a score function used to calculate various scores of transaction targets.

Note that the ranking information storage unit 23 may store various types of information, not limited to the above, depending on the purpose.

[3.3. processing unit 12]
The processing unit 12 is a controller, and for example, various programs (information processing An example of a program) is implemented by executing the RAM as a work area. Also, the processing unit 12 is a controller, and is implemented by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

As shown in FIG. 9, the processing unit 12 includes an acquiring unit 30, a receiving unit 31, an estimating unit 32, a determining unit 33, a generating unit 34, and a providing unit 35. Realize or perform the function or action of a process. Note that the internal configuration of the processing unit 12 is not limited to the configuration shown in FIG. 9, and may be another configuration as long as it performs information processing described later.

[3.3.1. Acquisition unit 30]
The acquisition unit 30 acquires various types of information. The acquisition unit 30 acquires various types of information from the storage unit 11 . The acquisition unit 30 acquires various types of information from the user information storage unit 20, the transaction target information storage unit 21, the content storage unit 22, the ranking information storage unit 23, and the like.

The acquisition unit 30 receives various types of information from an external information processing device via the communication unit 10 . The acquisition unit 30 receives various information from the terminal device 2 or the external device 3 . For example, the acquisition unit 30 acquires user information about the user U from the external device 3 . In FIG. 1, the acquisition unit 30 acquires user information such as attribute information of the user U, purchase history on Internet shopping, electronic shopping malls, auction sites, flea market sites, etc., display history of content, etc. Get from The acquisition unit 30 then stores the user information in the user information storage unit 20 . For example, the acquisition unit 30 acquires user information from the external device 3 and updates the user information stored in the user information storage unit 20 every predetermined period.

Also, the acquisition unit 30 acquires information about the transaction target from the external device 3 . For example, the acquisition unit 30 acquires from the external device 3 information about a transaction target traded at an auction site or a flea market site. Then, the acquisition unit 30 stores the information about the transaction object in the transaction object information storage unit 21 . For example, the acquisition unit 30 acquires information on the transaction object from the external device 3 and updates the information on the transaction object stored in the transaction object information storage unit 21 every predetermined period.

The acquisition unit 30 acquires transaction target information regarding a transaction target. The acquisition unit 30 acquires transaction target information including the sales history of the transaction target. The acquisition unit 30 acquires transaction target information including the number of orders for the transaction target. The acquisition unit 30 acquires transaction object information including a period that has passed since the date and time when the transaction object was sold. The acquisition unit 30 acquires transaction target information including the number of sales of the transaction target. The acquisition unit 30 acquires transaction target information including the number of times information indicating the transaction target has been browsed.

The acquisition unit 30 acquires transaction target information including the number of reviews for the transaction target. The acquisition unit 30 acquires transaction target information including an evaluation of the transaction target. The acquisition unit 30 acquires transaction target information including external factors related to the transaction target. The acquisition unit 30 acquires transaction target information including posts about transaction targets on the SNS. The acquisition unit 30 acquires transaction target information including the number of searches related to the transaction target. The acquisition unit 30 acquires transaction target information including internal factors related to the transaction target. The acquisition unit 30 acquires transaction target information including an incentive for the transaction target. The acquisition unit 30 acquires transaction target information including coupons for transaction targets. The acquisition unit 30 acquires transaction target information including price reductions related to transaction targets.

The acquisition unit 30 acquires element information regarding a plurality of elements included in transaction target information. The acquisition unit 30 acquires element information indicating tendencies of a plurality of elements included in transaction target information. Acquisition unit 30 acquires a score corresponding to each of the plurality of elements.

The acquisition unit 30 acquires element information indicating trends of a plurality of elements including the number of orders to be traded. The acquisition unit 30 acquires element information indicating the tendency of a plurality of elements including the number of views of information indicating a transaction target. The acquisition unit 30 acquires element information indicating trends of a plurality of elements including the number of reviews for the transaction target. The acquisition unit 30 acquires element information indicating trends of a plurality of elements including the number of reservations for transactions.

The acquisition unit 30 acquires element information indicating trends of a plurality of elements including external factors related to the transaction target. The acquisition unit 30 acquires element information indicating a tendency of a plurality of elements including posts related to transaction targets on the SNS. The acquisition unit 30 acquires element information indicating trends of a plurality of elements including the number of searches for a transaction target. The acquisition unit 30 acquires element information indicating trends of a plurality of elements including internal factors related to the transaction target. The acquisition unit 30 acquires element information indicating trends of a plurality of elements including incentives targeted at trade targets. The acquisition unit 30 acquires element information indicating trends of a plurality of elements including coupons targeted for transactions. The acquisition unit 30 acquires element information indicating a tendency of a plurality of elements including price reductions related to transaction objects.

In addition, the acquisition unit 30 acquires from the terminal device 2 information indicating the number of times the transaction target has been displayed (the number of times the user U has browsed). For example, the acquisition unit 30 acquires from the terminal device 2 the number of times the content has been displayed and the number of times each transaction object has been displayed. In addition, the acquisition unit 30 acquires control information for acquiring the number of times of display provided in advance together with the content. Then, the acquisition unit 30 stores the display count in the transaction target information storage unit 21 .

[3.3.2. reception unit 31]
The reception unit 31 receives various requests. The reception unit 31 receives various requests from an external information processing device. The reception unit 31 receives information indicating various requests from an external information processing device via the communication unit 10 . For example, the receiving unit 31 receives requests from the terminal device 2 or the external device 3 . The accepting unit 31 accepts a search query including the search keyword input by the user U from the terminal device 2 . For example, the receiving unit 31 receives a search query including a search keyword input by the user U from the terminal device 2 as a request for providing content related to the search query. Further, the receiving unit 31 receives a query including parameters from the terminal device 2 as a content provision request.

[3.3.3. Estimation unit 32]
The estimation unit 32 executes estimation processing for estimating various information. For example, the estimation unit 32 executes estimation processing based on various information acquired by the acquisition unit 30 . The estimation unit 32 executes estimation processing based on various information stored in the storage unit 11 . The estimation unit 32 performs estimation processing based on a search query received from the terminal device 2, various information received from an external information processing device, and the like.

For example, the estimation unit 32 estimates a query intent, which is the intent of the search query received by the reception unit 31 . The estimation unit 32 estimates, for example, the degree of relevance of the search query to each of the plurality of intention candidates as the query intention. The intention candidate is, for example, the category of the transaction object, the brand of the transaction object, the usage scene of the transaction object, the specifications of the transaction object, the price range of the transaction object, or the usage layer of the transaction object.

Based on the search keywords included in the search query, the estimation unit 32 calculates a score indicating the degree of relevance of the search query to each of the plurality of intention candidates, and treats these scores or information according to the scores as the query intention. The estimation unit 32 may, for example, use an estimation model for each intention candidate to calculate a score indicating the degree of relevance of the search query to each intention candidate, and estimate these scores or information according to the score as the query intention. can. The estimation model is a trained model generated by machine learning, receives a search keyword included in a search query, and outputs a score indicating the degree of relevance of the search query to the intention candidate.

An estimation model for each intention candidate is generated by machine learning using a neural network such as a convolutional neural network or a recurrent neural network, but is not limited to such examples. For example, an estimated model for each intention candidate may be generated using machine learning using a learning algorithm such as linear regression or logistic regression instead of using a neural network.

The estimation unit 32 also uses a table for each intention candidate in which each search keyword and a score indicating the degree of relevance are associated with each other, for example, and calculates the relevance of the search query for each of the plurality of intention candidates as the intent of the search query. can also be estimated as

In addition to the search keyword included in the search query, the estimating unit 32 estimates the query intention based on the user information of the search user who is the user U who entered the search keyword included in the search query. can also For example, the estimation unit 32 calculates a score indicating the degree of relevance of the search query to each of the plurality of intention candidates based on the search keyword and the user information of the search user included in the search query, and calculates these scores. Treat as the intent of the search query.

Also in this case, the estimation unit 32, for example, uses an estimation model for each intention candidate to calculate a score indicating the degree of relevance of the search query to each intention candidate, and uses these scores or information according to the score as the query intention. can be estimated. The estimation model is a trained model generated by machine learning. It takes as input the search keywords included in the search query and the user information of the search user, and outputs a score that indicates the degree of relevance of the search query to the intention candidate. .

Further, the estimating unit 32, for example, based on the search keyword included in the search query received by the receiving unit 31 before the previous time and the search keyword included in the search query received this time by the receiving unit 31, the query intent is determined. can also be estimated.

Also in this case, the estimation unit 32, for example, uses an estimation model for each intention candidate to calculate a score indicating the degree of relevance of the search query to each intention candidate, and uses these scores or information according to the score as the query intention. can be estimated. The estimation model is a trained model generated by machine learning. It takes as input the search keywords included in the previous search query and the current search query, and outputs a score that indicates the degree of relevance of the search query to the intention candidate. do. Note that instead of the estimation model for each intention candidate, one estimation model that outputs a score indicating the degree of relevance of a search query to a plurality of intention candidates may be used.

The estimating unit 32 can also estimate the query intention by dividing the score indicating the degree of relevance of the search query with respect to each of the plurality of intention candidates into a plurality of ranges. For example, the estimating unit 32 classifies the score indicating the degree of relevance of the search query to each category and brand into M stages. M is an integer of 2 or more.

The estimation unit 32 can also determine the degree of specificity of the query intention based on the score indicating the degree of relevance of the search query to each of the plurality of intention candidates. For example, the estimation unit 32 determines that the specificity of the query intention is relatively high when the intention candidate is intention, and the specificity of the query intention is relatively low when the intention candidate is no intention. . The estimation unit 32 can also estimate that the specificity of the query intention is relatively high, for example, when the intention candidate is specified.

Note that the estimation unit 32 can also determine the specificity of the query intention numerically, instead of determining the specificity of the query intention based on whether the specificity is high or low. For example, the estimating unit 32 can set the highest score among the scores indicating the degree of relevance of the search query to each of the plurality of intention candidates as the specificity of the query intention, and the search query for each of the plurality of intention candidates The average value of the scores indicating the degree of relevance of the query can also be used as the degree of specificity of the query intent.

[3.3.4. decision unit 33]
The determination unit 33 executes determination processing for determining various pieces of information. For example, the determining unit 33 determines the types and order of the plurality of ranking information to be provided to the user U, based on the query intent estimated by the estimating unit 32 . The classification of the ranking information is defined, for example, by the ranking cutoff and the display format of the ranking information. Note that the number of ranking candidates included in the ranking information is also defined in the type of ranking information.

The determination unit 33 can determine, for example, the order of arrangement of the plurality of ranking information so that ranking information having a higher degree of relevance to the query intent estimated by the estimation unit 32 is preferentially ranked higher. For example, when the estimating unit 32 calculates the score indicating the degree of relevance of the search query to each of the plurality of intention candidates, the determining unit 33 determines the order of arrangement of the plurality of ranking information based on these scores.

Further, when the estimating unit 32 classifies the score indicating the degree of relevance of the search query to each of the plurality of intention candidates into a plurality of ranges, the determining unit 33 determines a plurality of determines the sort order of the ranking information.

Further, when the estimation unit 32 classifies the score indicating the degree of relevance of the search query with respect to each of the plurality of intention candidates into a plurality of ranges, the determination unit 33 It is also possible to determine the order in which a plurality of pieces of ranking information are arranged based on the presence or absence of brand designation.

In this case, the determining unit 33 determines whether there is category designation and whether there is brand designation based on the search keyword included in the search query. Then, the determination unit 33 uses the case classification determination information (see, for example, FIG. 4) and the ranking information determination information (see, for example, FIG. 5) stored in the ranking information storage unit 23 and acquired by the acquisition unit 30. , based on the result of classification by the estimating unit 32, the arrangement order of the plurality of ranking information can be determined.

Note that the case classification determination information and the ranking information determination information are based on the browsing history of the user U for ranking information and the browsing history (for example, click history) of the user U for ranking elements included in the ranking information. , may be changed by the processing unit 12 . The determination unit 33 can also determine the type and order of ranking information using a decision model that inputs the query intent estimated by the estimation unit 32 and outputs the type and order of ranking information. The decision model, like the estimation model, is generated by machine learning using a neural network such as a convolutional neural network or a recurrent neural network, but is not limited to such examples.

Further, when it is determined that each of the plurality of intention candidates has no intention, the determination unit 33 preferentially ranks information on the intention candidates among the plurality of intention candidates, which are highly likely to be confirmed by the user U. Decide the order of the top order. For example, when the category intention and the brand intention are each "no intention", the determining unit 33 determines the sorting order in which the ranking information by category is prioritized over the ranking information by brand. Note that the ranking information about the intention candidates that the user U is likely to check is, for example, ranking information with a high possibility (for example, viewing frequency) that the ranking element will be viewed (eg, clicked). is not limited to

Based on the query intent, the determination unit 33 selects a plurality of display formats including a first display format in which the plurality of ranking elements constituting the ranking information are arranged in a line and a second display format in which the plurality of ranking elements are arranged in a matrix. The display format of the ranking information is determined from the display formats.

For example, the determination unit 33 determines the display format of the ranking information to be the first display format when the specificity of the query intention is relatively high, and the display format of the ranking information when the specificity of the query intention is relatively low. is determined to be the second display format.

[3.3.5. generation unit 34]
The generation unit 34 executes generation processing for generating various information. For example, the generation unit 34 executes generation processing based on various information acquired by the acquisition unit 30, various information stored in the storage unit 11, or various information received from an external information processing device.

The generation unit 34 generates, for example, the content C described above including the first tab TB1, the second tab TB2, the search box BX1, the first tab content C1, and the second tab content C2.

For example, the generation unit 34 generates the first tab content C1 in which search results corresponding to search queries including search keywords input by the user U are arranged. The generation unit 34 generates the first tab content C1 in which the search results obtained by the search process using the search keyword input by the user U are arranged.

The search process is performed, for example, by extracting transaction targets from transaction target information including search keywords. For example, when the search keyword included in the search query is “television”, the generating unit 34 extracts transaction target information corresponding to the search query “television” from the transaction target information storage unit 21 in the search process. It should be noted that the extraction of information corresponding to the search query is performed using a conventional technique, and any processing may be used as long as the transaction target can be specified, and detailed description thereof will be omitted.

Also, when the search process is performed by a device (search device) other than the information processing device 1, the acquisition unit 30 transmits a search query to the search device and receives search results of the search query from the search device. The generation unit 34 generates a search result of the search query using the transaction target information extracted as information corresponding to the search query. For example, the generating unit 34 generates the list information of the trading object of the extracted trading object information as the search result of the search query.

Also, the generation unit 34 executes generation processing based on the information determined by the determination unit 33 . For example, the generation unit 34 executes the generation process based on the ranking information types and order determined by the determination unit 33 .

The generation unit 34 generates a plurality of pieces of ranking information based on the type and order of the ranking information determined by the determination unit 33 and the transaction target information regarding the transaction target. For example, the generation unit 34 performs a process of calculating a score for each transaction target that is a ranking element in terms of the type of ranking information determined by the determination unit 33 for each determination type.

For example, the generation unit 34 uses a function (score function) that outputs the score of the transaction target with input of the transaction target information corresponding to the cut indicated by the ranking information type determined by the determination unit 33. to calculate the score of the transaction target. For example, the generation unit 34 calculates the average value of the partial scores corresponding to each of the plurality of elements included in the transaction object information as the score of the transaction object. For example, the generating unit 34 calculates the score of the transaction object by summing the partial scores corresponding to each of the plurality of elements included in the transaction object information.

The generation unit 34 uses the calculated score to rank the transaction objects for each determination type, and generates ranking information in which a plurality of transaction objects are arranged in the display format indicated by the determination type for each determination type. For example, the generation unit 34 ranks the transaction objects in descending order of the score, and arranges the transaction objects in order from the highest score in the display format determined by the determination unit 33, thereby generating the ranking information. .

Then, the generation unit 34 generates the second tab content C2 in which the plurality of ranking information items are arranged in the order determined by the determination unit 33 . For example, when the display format is a list format (an example of a first display format), the generating unit 34 generates a plurality of transaction information (ranking element), set a large amount of information for each.

The generation unit 34 appropriately uses various techniques related to image generation and image processing to generate a screen (content C) to be provided to the terminal device 2 . For example, the generation unit 34 appropriately uses various techniques such as Java (registered trademark) to generate a screen (content C) to be provided to the terminal device 2 . Note that the generation unit 34 may generate a screen (content C) to be provided to the terminal device 2 based on CSS, JavaScript (registered trademark), or HTML format. In addition, for example, the generation unit 34 may generate screens (contents) in various formats such as JPEG (Joint Photographic Experts Group), GIF (Graphics Interchange Format), and PNG (Portable Network Graphics).

[3.3.6. Providing unit 35]
The providing unit 35 provides various information. The providing unit 35 transmits various types of information to an external information processing device via the communication unit 10 . The providing unit 35 transmits various information to the terminal device 2 or the external device 3 . The providing unit 35 transmits content to the terminal device 2 . The providing unit 35 transmits information about the transaction target to the terminal device 2 .

For example, the providing unit 35 provides information generated by the generating unit 34 . For example, the providing unit 35 provides content C generated by the generating unit 34 . Provision of the content C is performed by transmitting the content C to the terminal device 2, for example. Note that the providing unit 35 can independently provide the same content as the first tab content C1 and the same content as the second tab content C2 instead of the content C.

The content transmitted from the providing unit 35 to the terminal device 2 is displayed on the terminal device 2 . For example, the terminal device 2 can display the content C provided by the providing unit 35 as shown in FIG.

[4. Processing procedure]
Next, the procedure of information processing executed by the information processing apparatus 1 according to the embodiment will be described with reference to FIG. 13 . FIG. 13 is a flowchart showing an example of the flow of information processing executed by the information processing apparatus 1. As shown in FIG.

As shown in FIG. 13, the processing unit 12 of the information processing device 1 receives a search query from the terminal device 2 via the network N and the communication unit 10 (step S10). Then, the processing unit 12 estimates the search intent based on the search query received in step S10 (step S11). For example, in the process of step S11, the processing unit 12 estimates the degree of relevance of the search query to each of the plurality of intention candidates as the intention of the search query.

Next, the processing unit 12 determines whether or not the intention candidate is specified (step S12). For example, the processing unit 12 determines whether or not there is designation of a category and whether or not there is designation of a brand.

Next, the processing unit 12 determines the type and order of the ranking information based on the query intent estimated in step S11 and whether or not the intent candidate is designated in step S12 (step S13).

Next, the processing unit 12 generates the content C including the second tab content C2 in which the ranking information of the type determined in step S13 is arranged in the order determined in step S13 (step S14). Then, the processing unit 12 provides the content C generated in step S14 (step S15), and ends the processing shown in FIG.

[5. Modification]
The information processing apparatus 1 described above may be implemented in various different forms other than the above embodiment. Therefore, other embodiments of the information processing apparatus 1 will be described below.

[5.1. User U]
In the above embodiment, the user U who is registered as a member of a predetermined service has been described as an example, but it is not necessary to be limited to the above example. For example, the user U may be any user U, for example, a user U who is not registered as a member of a predetermined service.

[5.2. Transaction target]
In the above embodiment, an example of a transaction target that is traded on an electronic shopping mall has been described, but the above example does not have to be limited. For example, the trading object may be any trading object. For example, the trading object may be a limited quantity trading object. Also, the transaction object may be a transaction object that is traded through Internet shopping, an auction site, a flea market site, or the like. Further, the search target and ranking target by the search query are not limited to the transaction target, and may be other than the transaction target.

[5.3. Provide information on the transaction object]
In the above-described embodiment, an example of information processing in which the information processing device 1 provides content to the terminal device 2 has been described, but it is not necessary to be limited to the above example. For example, an external server may provide content to the terminal device 2 . In this case, the information processing device 1 may provide the external server with information on the transaction target.

For example, the external server provides content regarding portal sites as an example of content. In this case, the information processing device 1 may provide the external server with an image of the transaction object, information on the layout when arranged in the content, etc., as the information on the transaction object.

[6. Hardware configuration]
The information processing device 1 or the terminal device 2 according to the embodiments described above is implemented by a computer 80 configured as shown in FIG. 14, for example. The information processing apparatus 1 will be described below as an example. FIG. 14 is a hardware configuration diagram showing an example of a computer 80 that implements the functions of the information processing apparatus 1 according to the embodiment. The computer 80 includes a CPU 81, a RAM 82, a ROM (Read Only Memory) 83, a HDD (Hard Disk Drive) 84, a communication interface (I/F) 85, an input/output interface (I/F) 86, and a media interface (I/F). ) 87.

The CPU 81 operates based on programs stored in the ROM 83 or HDD 84 and controls each section. The ROM 83 stores a boot program executed by the CPU 81 when the computer 80 is started, a program depending on the hardware of the computer 80, and the like.

The HDD 84 stores programs executed by the CPU 81, data used by these programs, and the like. The communication interface 85 receives data from other devices via the network N (see FIG. 9), sends the data to the CPU 81, and transmits data generated by the CPU 81 to other devices via the network N. FIG.

The CPU 81 controls output devices such as a display and a printer and input devices such as a keyboard and a mouse through an input/output interface 86 . The CPU 81 acquires data from the input device via the input/output interface 86 . The CPU 81 also outputs the generated data to the output device via the input/output interface 86 .

The media interface 87 reads programs or data stored in the recording medium 88 and provides them to the CPU 81 via the RAM 82 . The CPU 81 loads the program from the recording medium 88 onto the RAM 82 via the media interface 87 and executes the loaded program. The recording medium 88 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase change rewritable disc), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. and so on.

For example, when the computer 80 functions as the information processing apparatus 1 according to the embodiment, the CPU 81 of the computer 80 implements the functions of the processing unit 12 by executing programs loaded on the RAM 82 . Further, the data in the storage unit 11 is stored in the HDD 84 . The CPU 81 of the computer 80 reads these programs from the recording medium 88 and executes them, but as another example, these programs may be acquired via the network N from another device.

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

Also, each component of each device illustrated is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution and integration of each device is not limited to the one shown in the figure, and all or part of them can be functionally or physically distributed and integrated in arbitrary units according to various loads and usage conditions. Can be integrated and configured.

Also, the above-described embodiments and modifications can be appropriately combined within a range that does not contradict the processing content. For example, the terminal device 2 can partially determine the types and order of the plurality of ranking information, and in this case, the information processing device 1 and part of the terminal device 2 function as the information processing device.

[8. effect〕
As described above, the information processing apparatus 1 according to the embodiment includes the reception unit 31, the estimation unit 32, the determination unit 33, and the provision unit 35. The reception unit 31 receives search queries. The estimation unit 32 estimates the intent of the search query received by the reception unit 31 . The determining unit 33 determines the display format of ranking information based on the intent of the search query estimated by the estimating unit 32 . The providing unit 35 provides ranking information in the display format determined by the determining unit 33 . Thereby, the information processing device 1 can more appropriately provide the ranking information corresponding to the search query.

Further, the determining unit 33 selects one of a plurality of display formats including a first display format in which the plurality of ranking elements constituting the ranking information are arranged in a line and a second display format in which the plurality of ranking elements are arranged in a matrix. Determines the display format of the ranking information from. Thereby, the information processing device 1 can provide the ranking information corresponding to the search query in a more appropriate display format.

In addition, the ranking information provided by the providing unit 35 has a larger amount of information for each of the plurality of ranking elements when the display format is the first display format than when the display format is the second display format. be. Thereby, the information processing device 1 can provide the ranking information corresponding to the search query in a more appropriate display format.

Further, the determining unit 33 determines the display format of the ranking information to be the first display format when the specificity of the intent of the search query is relatively high, and determines the ranking information to be the first display format when the specificity of the intent of the search query is relatively low. The display format of the information is determined to be the second display format. Thereby, the information processing device 1 can provide the ranking information corresponding to the search query in a more appropriate display format.

In addition, the estimation unit 32 estimates the degree of relevance of the search query to each of the plurality of intention candidates as the intent of the search query, and the determination unit 33 ranks based on the degree of relevance of the search query to each of the plurality of intention candidates. Determines how information is displayed. Thereby, the information processing device 1 can provide the ranking information corresponding to the search query in a more appropriate display format.

Also, the ranking information includes information on two or more transaction targets among the plurality of transaction targets according to the intention of the search query. The plurality of candidate intents includes a category to be traded and a brand to be traded. As a result, the information processing device 1 can more accurately estimate the intent of the search query, and can more appropriately provide ranking information corresponding to the search query.

In addition, the providing unit 35 provides ranking information each including information on two or more transaction targets to be traded in electronic commerce. As a result, the information processing device 1 can more appropriately provide ranking information that ranks the transaction objects that are traded in electronic commerce.

In addition, the providing unit 35 transmits the ranking information to the terminal device 2 of the user U. Thereby, the information processing device 1 can more appropriately provide the user U with ranking information corresponding to the search query.

The providing unit 35 also includes a first tab TB1 that causes the terminal device 2 to display search results corresponding to the search query, and a second tab TB2 that causes the terminal device 2 to display a plurality of ranking information corresponding to the search query. Provide content C. Thereby, the information processing apparatus 1 can more appropriately provide the user U with a plurality of pieces of ranking information corresponding to the search query according to the user U's selection.

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

Also, the above-mentioned "section, module, unit" can be read as "means" or "circuit". For example, the acquisition unit can be read as acquisition means or an acquisition circuit.

1 information processing device 2 terminal device 3 external device 10 communication unit 11 storage unit 12 processing unit 20 user information storage unit 21 transaction target information storage unit 22 content storage unit 23 ranking information storage unit 30 acquisition unit 31 reception unit 32 estimation unit 33 determination unit 34 generation unit 35 provision unit 100 information processing system

Claims (11)

a reception unit that receives search queries;
A determination unit that determines a display format of multiple-element information, which is information in which a plurality of elements corresponding to the search query are arranged, based on the intent of the search query received by the reception unit;
and a providing unit that provides the plural-element information in the display format determined by the determining unit. The decision unit
Determining the display format of the plurality of elements information from among a plurality of display formats including a first display format in which the plurality of elements are arranged in a line and a second display format in which the plurality of elements are arranged in a matrix. The information processing apparatus according to claim 1, characterized by: In the multiple element information to be provided, when the display format is the first display format, a larger amount of information is set for each of the multiple elements than when the display format is the second display format. 3. The information processing apparatus according to claim 2. The decision unit
determining the display format of the multiple element information to be the first display format when the specificity of the intention of the search query is relatively high, and determining the multiple element information when the specificity of the intention of the search query is relatively low; 4. The information processing apparatus according to claim 2, wherein the display format of is determined to be the second display format. an estimating unit that estimates the degree of relevance of the search query to each of a plurality of intention candidates as the intent of the search query;
The decision unit
5. The information processing apparatus according to any one of claims 1 to 4, wherein a display format of said plurality of element information is determined based on the degree of relevance of said search query to each of said plurality of intention candidates. The multiple element information is
including information on two or more transaction targets according to the intent of the search query among a plurality of transaction targets;
The plurality of intention candidates are
6. The information processing apparatus according to claim 5, comprising the category of the transaction target and the brand of the transaction target. The providing unit
7. The information processing apparatus according to claim 6, wherein said plurality of element information each including information of said two or more transaction targets to be traded in electronic commerce is provided. The providing unit
The information processing apparatus according to any one of claims 1 to 7, wherein the plural element information is transmitted to a terminal device used by a user. The providing unit
providing content including a first tab that causes the terminal device to display search results corresponding to the search query, and a second tab that causes the terminal device to display the plurality of element information corresponding to the search query. 9. The information processing apparatus according to claim 8. A computer-executed information processing method comprising:
a receiving step for receiving a search query;
a determination step of determining, based on the intent of the search query received by the reception step, a display format of multi-element information, which is information in which a plurality of elements corresponding to the search query are arranged;
and a providing step of providing the plural-element information in the display format determined by the determining step. a reception procedure for receiving a search query;
a determination procedure for determining, based on the intent of the search query received by the reception procedure, a display format of multi-element information, which is information in which a plurality of elements corresponding to the search query are arranged;
and a providing step of providing the plural-element information in the display format determined by the determining step.

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