JP2018045332A - Estimation device, estimation method, and estimation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a device, method, and program that appropriately estimate the purpose of a query which is input by a user.SOLUTION: An estimation device 100 includes an acquisition unit 131 and an estimation unit 132. The acquisition unit 131 obtains a query used for search by a user who uses a terminal device. The estimation unit 132 estimates the purpose of the query, on the basis of the action history of the user before the user uses the query. For example, the estimation unit 132 estimates the purpose of the query on the basis of an input query which was input by the user prior to input of the query.SELECTED DRAWING: Figure 2

Description

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

  Conventionally, techniques for estimating various types of information related to search have been provided. For example, a technique related to ranking of search results obtained based on a query including many keywords is provided.

JP 2014-532928 A

  However, in the above-described conventional technology, it is not always possible to appropriately estimate the intention of the query input by the user. For example, in the case of a query including many keywords, it may be difficult to appropriately estimate the intention of the query input by the user due to a small number of searches performed using the query.

  The present application has been made in view of the above, and an object thereof is to provide an estimation device, an estimation method, and an estimation program for appropriately estimating the intention of a query input by a user.

  The estimation apparatus according to the present application determines an intention of the query based on an acquisition unit that acquires a query used for a search by a user using a terminal device, and an action history of the user before the user uses the query. An estimation unit for estimation.

  According to one aspect of the embodiment, there is an effect that it is possible to appropriately estimate the intention of the query input by the user.

FIG. 1 is a diagram illustrating an example of an estimation process according to the embodiment. FIG. 2 is a diagram illustrating a configuration example of the search device according to the embodiment. FIG. 3 is a diagram illustrating an example of a user information storage unit according to the embodiment. FIG. 4 is a diagram illustrating an example of the behavior information storage unit according to the embodiment. FIG. 5 is a diagram illustrating an example of the index information storage unit according to the embodiment. FIG. 6 is a diagram illustrating an example of a product information storage unit according to the embodiment. FIG. 7 is a flowchart illustrating an example of the estimation process according to the embodiment. FIG. 8 is a diagram illustrating an example of query intention estimation according to the embodiment. FIG. 9 is a diagram illustrating an example of query intention estimation based on transition probabilities according to the embodiment. FIG. 10 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the search device.

  Hereinafter, a mode for carrying out an estimation device, an estimation method, and an estimation program according to the present application (hereinafter referred to as “embodiment”) will be described in detail with reference to the drawings. In addition, the estimation apparatus, the estimation method, and the estimation program according to the present application are not limited by this embodiment. In the following embodiments, the same portions are denoted by the same reference numerals, and redundant description is omitted.

(Embodiment)
[1. (Estimation process)
An example of the estimation process according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of an estimation process according to the embodiment. In FIG. 1, the search device 100 estimates the intention of a query used by the user (hereinafter also simply referred to as “query intention”) based on the user's behavior history before the user uses the query. For example, the search device 100 estimates the intention of the query used by the user based on the history of queries used for searching for products provided in electronic commerce. Further, in FIG. 1, the search device 100 provides information related to the purchase target to an external information processing device based on the estimated query intention of the user.

  As illustrated in FIG. 1, the estimation system 1 includes a terminal device 10, an advertisement distribution device 50, and a search device 100. The terminal device 10, the advertisement distribution device 50, and the search device 100 are connected to be communicable by wire or wireless via a predetermined communication network (not shown). In addition, the estimation system 1 illustrated in FIG. 1 may include a plurality of terminal devices 10, a plurality of advertisement distribution devices 50, and a plurality of search devices 100.

  The terminal device 10 is an information processing device used by a user. The terminal device 10 is realized by, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like. In the example shown in FIG. 1, the case where the terminal device 10 is a smartphone used by the user is shown. Hereinafter, the terminal device 10 may be referred to as a user. That is, hereinafter, the user can be read as the terminal device 10. Specifically, FIG. 1 illustrates a case where the terminal device 10 is a smartphone used by a user identified by the user ID “U1” (hereinafter, may be referred to as “user U1”).

  In the example illustrated in FIG. 1, the terminal device 10 will be described as terminal devices 10-1 to 10-3 in accordance with the display of the screen of the terminal device 10. The terminal devices 10-1 to 10-3 are the same terminal device 10. Hereinafter, the terminal devices 10-1 to 10-3 are referred to as the terminal device 10 when they are not particularly distinguished.

  The search device 100 is an estimation device that acquires a query used by a user for a search and estimates the intention of the query based on a user's behavior history before the user uses the query. In addition, the search device 100 provides a search service that provides the terminal device 10 with a search result for the query acquired from the terminal device 10. In the example of FIG. 1, the search device 100 provides the terminal device 10 with a search result related to a product or service (hereinafter simply referred to as “product”) traded in a predetermined electronic commerce. In the example of FIG. 1, it is assumed that the search device 100 provides a predetermined electronic commerce service. Note that when the external information processing apparatus provides a predetermined electronic commerce service, the search apparatus 100 may not provide the search service. In this case, the search apparatus 100 acquires user behavior information from an external information processing apparatus or the like that provides the search service, and only estimates the query intention based on the acquired behavior information and provides the service based on the estimated query intention. May be performed.

  Further, in the example of FIG. 1, the search device 100 provides information specifying the user to the advertisement distribution device 50 when the user's query intention satisfies a predetermined condition. Specifically, in the example of FIG. 1, when the user's query intention is an intention related to purchase such as “gift”, the search apparatus 100 provides information specifying the user to the advertisement distribution apparatus 50. .

  Note that “* (* is an arbitrary numerical value)” following “dt” in each date and time “dt *” shown in the example of FIG. 1 indicates the date and time when the corresponding action or the like was performed, and the date and time “dt *”. The date becomes newer as the value of “*” is larger. For example, the date “dt12” indicates that the date is newer because the numerical value following “dt” is larger than the date “dt11”. That is, in the example illustrated in FIG. 1, the action performed by the user U1 at the date “dt12” is performed after the action performed by the user U1 at the date “dt11”.

  In the following, the search device 100 will be described as search devices 100-1 to 100-3 in accordance with the change in query intention of the user U1. That is, FIG. 1 shows that time has elapsed in the order of the search devices 100-1, 100-2, and 100-3. The search devices 100-1 to 100-3 are the same search device 100. Hereinafter, the search devices 100-1 to 100-3 will be referred to as the search device 100 when they are described without particular distinction. Hereinafter, the user information storage unit 121 will be described as user information storage units 121-1 to 121-3 in accordance with a change in the query intention of the user U <b> 1. That is, FIG. 1 shows that time has passed in the order of the user information storage units 121-1, 121-2, and 121-3. Further, the user information storage units 121-1 to 121-3 are the same user information storage unit 121. In the following description, the user information storage units 121-1 to 121-3 are referred to as the user information storage unit 121 when they are not particularly distinguished.

  The query intention here means the purpose of the user at the time when the user uses the query that is the target of the query intention estimation. For the sake of simplicity, FIG. 1 shows a case where the query intention of the user U1 is “gift” that is an intention related to purchase, but the query intention is not limited to an intention related to purchase. Various query intentions such as “comparison” and “kill time” may be used. Further, the query intention “−” of the user U1 shown in the user information storage unit 121-1 in FIG. 1 indicates a state where the query intention of the user U1 is not estimated. For example, the query intention “−” of the user U1 shown in the user information storage unit 121-1 in FIG. 1 indicates that the query is initialized, that is, the query that is the target of intention estimation is not input.

  First, the search device 100-1 acquires a query from the terminal device 10-1 used by the user U1 at the date and time dt11 (step S11). In the example of FIG. 1, the search device 100-1 acquires the query “wallet gift” from the terminal device 10-1. In the example shown in FIG. 1, the user U1 inputs the query “purse gift” in the search window in the page W11 of the mail order site A related to the electronic commerce service provided by the search device 100 displayed on the screen of the terminal device 10-1. And press the search button. Then, the terminal device 10-1 transmits a query “wallet gift” to the search device 100-1.

  The search device 100-1 that has acquired the query from the terminal device 10-1 provides the product information corresponding to the query to the terminal device 10-1 as a search result (step S12). In the example of FIG. 1, the search device 100-1 retrieves the product information corresponding to the query from the product information storage unit 124 (see FIG. 6) based on the information stored in the index information storage unit 123 (see FIG. 5). get. In the example of FIG. 1, the search device 100-1 acquires product information related to the product GD1 and the like corresponding to the query “wallet” from the product information storage unit 124. As described above, when “product GD * (* is an arbitrary numerical value)” is described, it indicates that the product is a product identified by the product ID “GD *”. For example, when “product GD1” is described, the product is a product identified by the product ID “GD1”. In the example of FIG. 1, the search device 100-1 sends the product information regarding the product GD1 and the like corresponding to the keyword “wallet” and the keyword “wallet” acquired from the product information storage unit 124 to the terminal device 10 as a search result. provide.

  The terminal device 10 provided with the search result including the product information related to the product GD1 from the search device 100-1 displays the search result (step S13). For example, a list of product information regarding the product GD1 is displayed on the terminal device 10, but the illustration is omitted in the example of FIG.

  In addition, the search device 100-1 estimates the query intention of the user U1 (step S14). In the example of FIG. 1, since the search device 100-1 has input the query “purse gift”, the query intention of the user U1 is estimated as “gift” corresponding to the intention of searching the wallet for gifts. . For example, the search device 100-1 estimates the query intention of the user who has input the query “wallet gift” as “gift” based on the action histories of many users who have previously input the query “wallet gift”. For example, when many of the users who have entered the query “wallet gift” in the past have purchased a wallet, the search device 100-1 uses the behavior information of the user and the keyword “gift” included in the query, The query intention of the user who entered the query “wallet gift” is estimated as “gift”. For example, the search device 100 may store a correspondence table regarding each keyword and the user's intention corresponding to the keyword, and estimate the user's query intention as a “gift” based on the correspondence table.

  The search device 100 that estimated the query intention of the user U1 as “gift” changes the information based on the estimated query intention (step S15). In the example of FIG. 1, the search device 100-2 changes the query intention of the user U <b> 1 stored in the user information storage unit 121 to “gift” as illustrated in the user information storage unit 121-2.

  Thereafter, the search device 100-2 acquires a query from the terminal device 10-2 used by the user U1 at the date dt12 (step S16). In the example of FIG. 1, the search device 100-2 acquires the query “wallet gift white” from the terminal device 10-2. In the example shown in FIG. 1, the user U1 makes a query “wallet gift white” in the search window in the page W11 of the mail order site A related to the electronic commerce service provided by the search device 100 displayed on the screen of the terminal device 10-2. Enter and press the search button. Then, the terminal device 10-2 transmits the query “wallet gift white” to the search device 100-2.

  The search device 100-2 that has acquired the query from the terminal device 10-2 provides the product information corresponding to the query to the terminal device 10-2 as a search result (step S17). In the example of FIG. 1, the search device 100-2 acquires product information corresponding to the query from the product information storage unit 124 based on the information stored in the index information storage unit 123. In the example of FIG. 1, the search device 100-2 obtains product information regarding the product GD 314 and the like corresponding to the keyword “wallet”, the keyword “gift”, and the keyword “white” from the product information storage unit 124. In the example of FIG. 1, the search device 100-2 searches the product information on the product GD 314 and the like corresponding to the keyword “wallet”, the keyword “gift”, and the keyword “white” acquired from the product information storage unit 124. To the terminal device 10.

  The terminal device 10 provided with the search result including the product information related to the product GD314 from the search device 100-2 displays the search result (step S18). For example, the terminal device 10 displays a list of product information and the like related to the product GD314, but is not shown in the example of FIG.

  In addition, the search device 100-2 estimates the query intention of the user U1 (step S19). In the example of FIG. 1, the search device 100-2 estimates that the query intention of the user U1 is “unknown”. For example, since the search device 100-2 has a small number of users who have previously entered the query “purse gift white”, the query “purse gift white” is based on the behavior history of the user who entered the query “purse gift white”. It is not possible to estimate the query intention of the user who entered For example, if the number of users who have entered the query “wallet gift white” in the past (for example, the most recent month) is less than a predetermined threshold (for example, less than 100 people, etc.), The query intention of the user who entered the query cannot be estimated, and the query intention of the user who entered the query “purse gift white” is estimated as “unknown”.

  The search device 100 that estimated the query intention of the user U1 as “unknown” takes over the query intention estimated immediately before (step S20). In the example of FIG. 1, the search device 100-3 maintains the query intention of the user U1 stored in the user information storage unit 121 as “gift” as illustrated in the user information storage unit 121-3. Thus, the search device 100 sets the query intention as the query intention estimated from the immediately preceding behavior information. That is, the search device 100 estimates “gift”, which is the query intention estimated by the query “purse gift”, as the query intention of the query “purse gift white”.

  Here, when the query intention of the user U1 is an intention related to the purchase such as “gift”, the search device 100 provides information for identifying the user U1 to the advertisement distribution device 50 as satisfying a predetermined condition. In the example of FIG. 1, the search device 100-3 provides information specifying the user U1 to the advertisement distribution device 50 based on the query intention of the user U1 “gift” (step S21). In addition, the search device 100-3 provides the advertisement distribution device 50 with information that specifies that the user U1 is looking for a white wallet as a gift. Although the illustration is omitted in the example of FIG. 1 for the sake of simplicity, the search device 100 also has a predetermined condition because the query intention of the user U1 is an intention related to purchase such as “gift” in step S15. If the condition is satisfied, information specifying the user U1 may be provided to the advertisement distribution device 50.

  Thereafter, an advertisement request is transmitted from the terminal device 10-3 to the advertisement distribution device 50 at the date and time dt13 (step S22). The advertisement distribution device 50 that acquired the advertisement request from the terminal device 10 sends the advertisement X to the terminal device 10-3 based on the information that the user U1 acquired from the search device 100 is searching for a white wallet as a gift. Distribute (step S23). For example, the advertisement distribution device 50 distributes the advertisement X related to the popular white wallet X as a gift to the terminal device 10-3.

  The terminal device 10-3 that has received the advertisement X from the advertisement distribution device 50 displays the advertisement X (step S24). For example, the terminal device 10 displays the page W12 including the advertisement X. Thereby, an advertisement suitable for the query intention of the user U1 is displayed on the terminal device 10. Note that the search device 100 may provide information that prompts the terminal device 10 to purchase a predetermined wallet. For example, the search device 100 may provide the advertisement X to the terminal device 10. For example, the search device 100 may provide the advertisement X to the terminal device 10 without providing information specifying the user U1 to the advertisement distribution device 50 in step S21. That is, the search device 100 may have the function of the advertisement distribution device 50, and the search device 100 may distribute the advertisement. In this case, the advertisement distribution device 50 may not be included in the estimation system 1.

  As described above, the search device 100 can appropriately estimate the intention of the query input by the user. Moreover, the terminal device 10 can display an advertisement suitable for the user's query intention. In the above example, for the sake of simplicity, the case where the user's action is a search is shown. However, the user's action is any action that can be an estimation target of the user's query intention. It may be. For example, the search device 100 may acquire various types of information such as browsing of the user's content as user behavior information, and may estimate the user's query intention based on the information. For example, when a query used at the end of a series of search actions (hereinafter also referred to as “tail query”) includes many keywords, the number of searches performed using the tail query is often small. For this reason, behavior information corresponding to the tail query is often insufficient, and it may be difficult to appropriately estimate the intention of the query input by the user. In addition, the user rarely inputs a combination of a plurality of keywords corresponding to the tail query in the initial search, and often repeats the search by gradually inputting keywords (for example, one by one). Therefore, the search device 100 appropriately determines the intention of the query input by the user by estimating the intention of the query input by the user based on the intention of the other query input by the user before reaching the tail query. Can be estimated. In the example described above, the search device 100 estimates the query intention of the query “purse gift” input by the user U1 immediately before the query “purse gift white” as the query intention of the query “purse gift white”. Even when the query intention cannot be directly estimated from “purse gift white”, the intention of the query input by the user can be appropriately estimated. FIG. 8 shows another example in which keywords are gradually added until reaching the tail query.

  For example, when the user U1 performs a search using the query “5000 yen gift”, the search device 100 has a query intention corresponding to that the user U1 is searching for a 5000 yen product as a gift. It may be estimated. In addition, for example, when the behavior information in which the user U1 continuously browses the content on which the product of about 5000 yen is posted is acquired, the search device 100 searches the user U1 for the query intention of 5000 yen. It may be estimated that the query intention corresponds to the In this case, the search device 100 may provide the terminal device 10 and the advertisement distribution device 50 with information related to a product of 5000 yen. For example, the search device 100 may provide the terminal device 10 with an advertisement related to a product of 5000 yen, or cause the advertisement distribution device 50 to distribute an advertisement about a product of 5000 yen to the terminal device 10. Further, the search device 100 may estimate the intention of the query input by the user based on information obtained by abstracting each query. For example, the search device 100 may generate a vector corresponding to each query, and estimate the intention of the query input by the user based on the similarity between the vectors. For example, the search device 100 may estimate the intention of the query input by the user based on the similarity between vectors corresponding to each query by learning distributed expressions. For example, when the similarity between the vector of the query “wallet gift white” and the vector of the query “wallet gift” satisfies a predetermined condition, the search device 100 sets the query intention of the query “wallet gift” to the query “wallet gift”. It may be estimated that the query intention is “white”. In this way, when the query “wallet gift white” vector is similar to the query “wallet gift” vector, the search device 100 determines the query intention of the query “wallet gift” and the query “wallet gift white”. It may be estimated that the query is intended.

[2. Configuration of estimation device]
Next, the configuration of the search device 100 according to the embodiment will be described with reference to FIG. FIG. 2 is a diagram illustrating a configuration example of the search device according to the embodiment. As illustrated in FIG. 2, the search device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The search device 100 includes an input unit (for example, a keyboard and a mouse) that receives various operations from an administrator of the search device 100 and a display unit (for example, a liquid crystal display) for displaying various types of information. May be.

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card). The communication unit 110 is connected to the network by wire or wirelessly, and transmits and receives information to and from the terminal device 10 and the advertisement distribution device 50 included in the estimation system 1, for example.

(Storage unit 120)
The storage unit 120 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. As illustrated in FIG. 2, the storage unit 120 according to the embodiment includes a user information storage unit 121, a behavior information storage unit 122, an index information storage unit 123, and a product information storage unit 124.

(User information storage unit 121)
The user information storage unit 121 according to the embodiment stores various types of information regarding user attributes. For example, the user information storage unit 121 stores user attribute information. FIG. 3 is a diagram illustrating an example of a user information storage unit according to the embodiment. The user information storage unit 121 illustrated in FIG. 3 includes items such as “user ID”, “age”, “gender”, and “query intention”.

  “User ID” indicates identification information for identifying a user. For example, the user identified by the user ID “U1” corresponds to the user U1 illustrated in the example of FIG. “Age” indicates the age of the user who uses the terminal device 10. The “age” may be a specific age of the user identified by the user ID, such as 35 years old. “Gender” indicates the gender of the user who uses the terminal device 10. “Query intention” indicates a query intention of a user who uses the terminal device 10.

  For example, in the example illustrated in FIG. 3, the age of the user identified by the user ID “U1” is “20s”, and the gender of the user is “female”. In addition, for example, the user identified by the user ID “U1” indicates that the query intention is “−”, that is, the query that is the target of intention estimation is not input. Further, for example, in the example illustrated in FIG. 3, the age of the user identified by the user ID “U2” is “30s”, and the gender of the user is “male”. Further, for example, the user identified by the user ID “U2” indicates that the query intention is “−”, that is, the query that is the target of intention estimation is not input.

  The user information storage unit 121 is not limited to the above, and may store various types of information according to the purpose. For example, the user information storage unit 121 may store information on demographic attributes of users and information on psychographic attributes. For example, the user information storage unit 121 may store information such as name, family structure, income, interest, and lifestyle.

(Behavior information storage unit 122)
The behavior information storage unit 122 according to the embodiment stores various types of information regarding the user's behavior. FIG. 4 is a diagram illustrating an example of the behavior information storage unit according to the embodiment. For example, the behavior information storage unit 122 stores user behavior information for content distributed to the terminal device 10 of each user. The action information storage unit 122 illustrated in FIG. 4 includes items such as “user ID”, “action ID”, “type”, “content”, and “date and time”.

  “User ID” indicates identification information for identifying a user. For example, the user identified by the user ID “U1” corresponds to the user U1 illustrated in the example of FIG. The “behavior ID” indicates information for identifying the user's behavior. The “type” indicates information related to the type of action of the corresponding user. Further, “content” indicates content that is targeted in the corresponding user's action. “Date and time” indicates the date and time when the corresponding user action was performed.

  For example, in the example illustrated in FIG. 4, the user (user U1) identified by the user ID “U1” indicates that the action AC5 or the like has been performed. Further, for example, in the example illustrated in FIG. 4, the user U1 indicates that an action identified by the action ID “AC11” or an action identified by the action ID “AC12” has been performed. Specifically, in the example illustrated in FIG. 4, the user U1 indicates that a search using the query “purse gift” (action AC11), a search using the query “purse gift white” (action AC12), and the like is performed. .

  The behavior information storage unit 122 is not limited to the above, and may store various types of information according to the purpose. Moreover, although the case where action information was memorize | stored in the action information storage part 122 for every user ID was shown in FIG. 4, action information may be memorize | stored not only for every user ID but in order of time, for example.

(Index information storage unit 123)
The index information storage unit 123 according to the embodiment stores various types of information related to the index. FIG. 5 is a diagram illustrating an example of the index information storage unit according to the embodiment. The index information storage unit 123 illustrated in FIG. 5 includes items such as “index ID”, “index”, and “target product”. The “target product” has items such as “# 1” and “# 50”.

  “Index ID” indicates identification information for identifying an index. “Index” indicates an index. “# 1”, “# 50”, etc. in the “target product” indicate product information corresponding to the index. Note that “# 1”, “# 50”, and the like may correspond to the order of product information corresponding to the index. For example, “# 1” in “target product” may be product information ranked “1st” among the product information corresponding to the index. Further, “# 50” in the “target product” may be product information ranked “50th” in the product information corresponding to the index.

  In the example of FIG. 5, the index “wallet” identified by the index ID “IN11” indicates that the corresponding product information is product information related to the product GD1, the product GD314, and the like. In the example of FIG. 5, the index “gift” identified by the index ID “IN12” indicates that the corresponding product information is product information regarding the product GD314, the product GD1, and the like. In the example of FIG. 5, the index “white” identified by the index ID “IN13” indicates that the corresponding product information is product information regarding the product GD256, the product GD314, and the like.

  The index information storage unit 123 is not limited to the above, and may store various information according to the purpose. For example, the index information storage unit 123 may store information related to the date and time when each index or each item information is added.

(Product information storage unit 124)
The product information storage unit 124 according to the embodiment stores various types of information related to products. FIG. 6 is a diagram illustrating an example of a product information storage unit according to the embodiment. The product information storage unit 124 illustrated in FIG. 6 includes items such as “product ID”, “product”, “store”, “category”, “title”, and “description”.

  “Product ID” indicates identification information for identifying a product. “Product” indicates a specific name or the like of the product. “Store” indicates a provider that provides the corresponding product, that is, a seller. “Category” indicates the category to which the corresponding product belongs. “Title” indicates a title included in the product information of the product identified by the product ID. The “description” indicates an explanation included in the product information of the product identified by the product ID.

  For example, in the example illustrated in FIG. 6, the product (product GD1) identified by the product ID “GD1” is the product “wallet A”, which indicates that the product is provided by the store A. In addition, the category of the product GD1 indicates “fashion”. Further, the title of the product GD1 indicates “latest ...”. In addition, the description of the product GD1 indicates that “I will deliver with free shipping…”. For example, in the example illustrated in FIG. 6, the product (product GD2) identified by the product ID “GD2” is the product “clock A” and is a product provided by the store B. The category of the product GD2 indicates “accessory”.

  The product information storage unit 124 is not limited to the above, and may store various information according to the purpose. For example, the product information storage unit 124 may store information on the date and time when product information was added and the date and time when product information was created. The merchandise information storage unit 124 may store information regarding the evaluation value of each store.

(Control unit 130)
Returning to the description of FIG. 2, the control unit 130 is a controller and is stored in a storage device inside the search device 100 by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. Various programs (corresponding to an example of an estimation program) are implemented by using the RAM as a work area. The control unit 130 is a controller, and is realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  As illustrated in FIG. 2, the control unit 130 includes an acquisition unit 131, an estimation unit 132, a change unit 133, and a provision unit 134, and implements or executes information processing functions and operations described below. .

(Acquisition part 131)
For example, the acquisition unit 131 acquires various types of information. For example, the acquisition unit 131 acquires various types of information from the user information storage unit 121, the behavior information storage unit 122, the index information storage unit 123, and the product information storage unit 124.

  The acquisition unit 131 may acquire various types of information from an external information processing apparatus. For example, the acquisition unit 131 acquires a query. For example, the acquisition unit 131 acquires a query used for a search by a user using the terminal device 10. For example, the acquisition unit 131 acquires a query input at the end of a series of search actions performed by the user as a tail query. In the example of FIG. 1, the acquisition unit 131 acquires a query from the terminal device 10 used by the user U1. In the example of FIG. 1, the acquisition unit 131 acquires the query “wallet gift” from the terminal device 10-1. In the example of FIG. 1, the acquisition unit 131 acquires the query “wallet gift white” from the terminal device 10-2.

  In the example of FIG. 1, the acquisition unit 131 obtains product information corresponding to the query from the product information storage unit 124 (see FIG. 6) based on the information stored in the index information storage unit 123 (see FIG. 5). get. In the example of FIG. 1, the acquisition unit 131 acquires product information regarding the product GD1 and the like corresponding to the keyword “wallet” and the keyword “gift” from the product information storage unit 124. In the example of FIG. 1, the acquisition unit 131 acquires product information related to the product GD314 and the like corresponding to the keyword “wallet”, the keyword “gift”, and the keyword “white” from the product information storage unit 124.

(Estimation unit 132)
The estimation unit 132 estimates various information. For example, the estimation unit 132 estimates the user's query intention based on the user's behavior information. For example, the estimation unit 132 estimates the intention of the query based on the user's behavior history before the user uses the query. For example, the estimation unit 132 estimates the intent of the query based on the input query input by the user before inputting the query.

  In addition, for example, the estimation unit 132 is an input query input by a user before inputting a query including a plurality of character strings, and the query is based on an input query including a part of the plurality of character strings. Estimate the intention. In the example of FIG. 1, the estimation unit 132 is an input query input by the user before the input of the query “purse gift white” including a plurality (three) of character strings (keywords). Estimate the intent of the query based on the input query “wallet gift” that contains some string. For example, the estimation unit 132 estimates whether the search based on the query is a search related to purchase as the intention of the query.

  In the example of FIG. 1, the estimation unit 132 estimates the query intention of the user U1. For example, since the estimation unit 132 inputs the query “wallet gift”, the estimation intention of the user U1 is estimated as “gift” corresponding to the intention of searching for the wallet for gifts. For example, the estimation unit 132 estimates the query intention of the user who has input the query “wallet gift” as “gift” based on the action histories of many users who have previously input the query “wallet gift”. For example, if many of the users who have previously input the query “wallet gift” have purchased a wallet, the estimation unit 132 estimates the query intention of the user who has input the query “wallet gift” as “gift”.

  In the example of FIG. 1, the estimation unit 132 estimates the query intention of the user U1. For example, the estimation unit 132 estimates that the query intention of the user U1 is “unknown”. For example, the estimation unit 132 inputs the query “purse gift white” based on the behavior history of the user who has input the query “purse gift white” because the number of users who have previously input the query “purse gift white” is small. The user's query intention cannot be estimated. For example, the estimation unit 132 inputs the query “purse gift white” when the user who has entered the query “purse gift white” in the past (for example, the most recent month) is less than a predetermined threshold (for example, less than 100 people, etc.). The query intention of the user who entered the query “purse gift white” is estimated as “unknown”.

  In the example of FIG. 1, when the estimation unit 132 estimates that the query intention of the user U1 is “unknown”, the estimation unit 132 takes over the query intention estimated immediately before (step S20). In the example of FIG. 1, the estimation unit 132 maintains the query intention of the user U1 stored in the user information storage unit 121 as “gift” as illustrated in the user information storage unit 121-3. For example, the estimation unit 132 sets the query intention as the query intention estimated from the immediately preceding behavior information. That is, the search device 100 estimates “gift”, which is the query intention estimated by the query “purse gift”, as the query intention of the query “purse gift white”.

(Change unit 133)
The changing unit 133 changes various information. For example, the changing unit 133 changes the user's query intention. For example, the changing unit 133 changes the query intention based on the user's query intention estimated by the estimating unit 132.

  In the example of FIG. 1, the changing unit 133 changes the information on the query intention of the user U1 estimated by the estimating unit 132 based on “gift”. For example, the changing unit 133 changes the query intention of the user U1 stored in the user information storage unit 121 to “gift” as shown in the user information storage unit 121-2. For example, when the estimation unit 132 estimates that the query intention of the user U1 is “gift”, the changing unit 133 changes the information based on the estimated query intention. In the example of FIG. 1, the changing unit 133 changes the query intention of the user U1 stored in the user information storage unit 121 to “gift” as shown in the user information storage unit 121-2.

(Providing unit 134)
The providing unit 134 provides various types of information to an external information processing apparatus. For example, the providing unit 134 provides information related to the query when the estimation unit 132 estimates that the search based on the query is a search related to purchase. For example, when the estimation unit 132 estimates that the search based on the query is a search related to purchase, the providing unit 134 provides information prompting the user to purchase the target related to the query. For example, when the acquisition unit 131 acquires a query from the terminal device 10, the providing unit 134 provides product information corresponding to the query to the terminal device 10 as a search result.

  For example, when the user's query intention satisfies a predetermined condition regarding purchase, the providing unit 134 provides information that identifies the user to a target provider regarding purchase. In the example of FIG. 1, the providing unit 134 provides information specifying the user U1 to the advertisement distribution device 50 when the query intention of the user U1 is an intention related to purchase such as “gift”. For example, the providing unit 134 provides the advertisement distribution device 50 with information that specifies that the user U1 is looking for a wallet as a gift. In the example of FIG. 1, when the query intention of the user U1 is “unknown”, the providing unit 134 provides information when the query intention estimated immediately before the condition is satisfied as the query intention of the user U1. To do.

For example, the providing unit 134 may provide the terminal device 10 with information that prompts the user to purchase a predetermined wallet. For example, the providing unit 134 may provide the advertisement X to the terminal device 10.
[3. (Estimation process flow)
Here, the procedure of the estimation process by the search device 100 according to the embodiment will be described with reference to FIG. FIG. 7 is a flowchart illustrating an example of the estimation process according to the embodiment.

  As shown in FIG. 7, the search device 100 acquires a query (step S101). In the example of FIG. 1, the search device 100 acquires the query “wallet gift white” from the terminal device 10.

  The search device 100 estimates the intention of the query based on the action history of the user who has input the query (step S102). In the example of FIG. 1, the search device 100 maintains the query intention of the user U1 stored in the user information storage unit 121 as “gift”. In this case, the search device 100 sets the query intention as the query intention estimated from the immediately preceding behavior information. For example, the search device 100 estimates “gift”, which is the query intention estimated by the query “purse gift”, as the query intention of the query “purse gift white”. That is, the search device 100 estimates the intention of the query “purse gift white” based on a search by the query “purse gift white”, which is the user's action history before the user uses the query “purse gift white”.

  On the other hand, the search device 100 determines whether the intent of the query satisfies a predetermined condition (step S103). For example, when the user's query intention is an intention related to purchase such as “gift”, the search device 100 determines that the query intention satisfies a predetermined condition.

  For example, when the intent of the query does not satisfy a predetermined condition (Step S103: No), the search device 100 ends the process. For example, if the user's query intention is not an intention related to purchase such as “search”, the search device 100 ends the process.

  On the other hand, when the intent of the query satisfies a predetermined condition (step S103: Yes), the search device 100 provides a predetermined service (step S104). In the example of FIG. 1, the search device 100 determines that a predetermined condition is satisfied because the query intention of the user U1 is “gift”, and provides the advertisement distribution device 50 with information that identifies the user U1.

[4. Query intention estimation)
As described above, for example, when the intent of the query can be estimated based on the query, the search device 100 estimates the intent of the query based on the query. Further, for example, when the search device 100 cannot estimate the intention of the query based on the query, the search device 100 estimates the intention of the query based on the user's behavior history before the user uses the query. This point will be described in detail with reference to FIG. FIG. 8 is a diagram illustrating an example of query intention estimation according to the embodiment.

  In the example of FIG. 8, it is assumed that the date and time are newer in the direction of the arrow (from top to bottom). That is, in the example of FIG. 8, it is assumed that the query Q1 is the first input query, the query Q2 is the next input query after the query Q1, and the query Q3 is the next input query after the query Q2. In this case, in the example of FIG. 8, the query Q3 is the latest query. In the example of FIG. 8, it is assumed that the query Q3 is a tail query input at the end of a series of search actions performed by the user.

  For example, when a user who uses the terminal device 10 inputs a keyword in the search window BX displayed on the terminal device 10 and presses a search button or the like, the search device 100 transmits the keyword. In the following, a case where the user who uses the terminal device 10 is the user U1 will be described as an example.

  First, in the example of FIG. 8, the user U1 inputs a query “keyword A keyword B”, that is, a query Q1 including two keywords (query) of the keyword A and the keyword B in the search window BX displayed on the terminal device 10. To do. In FIG. 8, keywords (queries) are abstractly illustrated as keywords A to D, but each keyword corresponds to a character string indicating a specific target. For example, the keyword A may be “wallet”, the keyword B may be “5000 yen”, the keyword C may be “gift”, and the keyword D may be “white”.

  In the example of FIG. 8, it is assumed that there is a lot of action information corresponding to the query Q1, as shown in the corresponding action information DQ1. That is, it is assumed that the search device 100 that acquired the query Q1 from the terminal device 10 can estimate the query intention of the user U1 based on the query Q1. Therefore, the search device 100 estimates the query intention of the user U1 based on the query Q1 (step S51). In the example of FIG. 8, the search device 100 estimates the query intention of the user U1 as “search” as shown in the query intention information QA1.

  For example, the search device 100 estimates the query intention of the user who has input the query “keyword A keyword B” as “search” based on the action history of many users who have previously input the query “keyword A keyword B”. . For example, if many of the users who have entered the query “keyword A keyword B” in the past have ended only with search behavior, the search device 100 determines the query intention of the user who entered the query “keyword A keyword B” as “search ".

  Next, in the example of FIG. 8, the user U1 enters the query “keyword A keyword B keyword C” in the search window BX displayed on the terminal device 10, that is, three keywords (query) of keyword A, keyword B, and keyword C. A query Q2 including is entered.

  In the example of FIG. 8, it is assumed that the behavior information corresponding to the query Q2 is moderate, that is, an average amount is collected as shown in the corresponding behavior information DQ2. That is, it is assumed that the search device 100 that acquired the query Q2 from the terminal device 10 can estimate the query intention of the user U1 based on the query Q2. Therefore, the search device 100 estimates the query intention of the user U1 based on the query Q2 (step S52). In the example of FIG. 8, the search device 100 estimates that the query intention of the user U1 is “purchase”, as indicated by the query intention information QA2.

  For example, the search device 100 sets the query intention of the user who has input the query “keyword A keyword B keyword C” to “purchase” based on the behavior history of many users who have previously input the query “keyword A keyword B keyword C”. ". For example, in the case where many users who have entered the query “keyword A keyword B keyword C” in the past have purchased a wallet of around 5000 yen, the search device 100 has entered the query “keyword A keyword B keyword C”. Estimate the query intent to be “buy”.

  Next, in the example of FIG. 8, the user U 1 enters the query “keyword A keyword B keyword C keyword D”, that is, keyword A, keyword B, keyword C, and keyword D 4 in the search window BX displayed on the terminal device 10. A query Q3 including two keywords (queries) is input.

  In the example of FIG. 8, it is assumed that there is little action information corresponding to the query Q3 as shown in the corresponding action information DQ3. That is, it is assumed that the search device 100 that has acquired the query Q3 from the terminal device 10 cannot estimate the query intention of the user U1 based on the query Q3. Therefore, the search device 100 estimates that the query intention of the user U1 is unknown based on the query Q3 (step S53). In the example of FIG. 8, the search device 100 estimates the query intention of the user U1 as “? (Unknown)” as shown in the query intention information QA3.

  For example, the search apparatus 100 has a small number of users who have previously input the query “keyword A keyword B keyword C keyword D”. Therefore, based on the behavior history of the user who entered the query “keyword A keyword B keyword C keyword D”, the query intention of the user who entered the query “keyword A keyword B keyword C keyword D” cannot be estimated. For example, when the number of users who have entered the query “keyword A keyword B keyword C keyword D” in the past (for example, the most recent month) is less than a predetermined threshold (for example, less than 500 people), the search device 100 The query intention of the user who has input “A keyword B keyword C keyword D” cannot be estimated, and the query intention of the user who has input the query “keyword A keyword B keyword C keyword D” is estimated as “unknown”.

  The search device 100 that estimated the query intention of the user U1 as “unknown” estimates the query intention of the query Q3 based on the query intention estimated immediately before (step S54). In the example of FIG. 8, the search device 100 estimates that the query intention of the user U1 is “purchase” as shown in the query intention information QA3-2. That is, the search device 100 takes over the query intention of the query Q2 to the query intention of the query Q3, and maintains the query intention of the user U1 as “purchased”. Thus, the search device 100 sets the query intention as the query intention estimated from the immediately preceding behavior information. That is, the search device 100 estimates “purchase” which is a query intention estimated by the query “keyword A keyword B keyword C” as a query intention of the query “keyword A keyword B keyword C keyword D”. Thereby, the search device 100 can appropriately estimate the query intention of the query Q3, which is a tail query with little corresponding behavior information. The above keywords A to D are examples. For example, the keyword A is “TV”, the keyword B is “maker A” indicating the manufacturer of the TV, and the keyword C is a TV manufactured by the TV manufacturer. May be “Brand X” indicating the brand name, and the keyword D may be “42 type”.

[5. Query intention estimation based on transition probability)
Note that the search apparatus 100 is not limited to the above, and may use various information as appropriate to estimate the intention of the query. This point will be described in detail with reference to FIG. FIG. 9 is a diagram illustrating an example of query intention estimation based on transition probabilities according to the embodiment.

  First, in the example of FIG. 9, a case where the user finally inputs the query “purse gift white” in a series of search actions will be described as an example. A graph TG in FIG. 9 shows transition probabilities between the queries. For example, the arrow in the graph TG in FIG. 9 indicates the transition probability from the query located at the base of the arrow to the query located at the tip of the arrow. Note that the search device 100 calculates a transition relationship between queries such as the above-described graph TG based on a history of search actions (search logs) by many users. In addition, the search device 100 calculates a probability described later based on a history of search actions by many users.

  For example, the probability that the user who has input the query “gift white” indicated by the character string TM13 shown in FIG. 9 has input the query “gift” indicated by the character string TM1 immediately before the query “gift white” is “0. 9 ”, that is,“ 90% ”. Further, for example, the probability that the user who has input the query “gift white” shown in the character string TM13 shown in FIG. 9 inputs the query “white” shown in the character string TM2 immediately before the query “gift white” is “ 0.1 ", that is," 10% ".

  Further, the search device 100 estimates that the query intention of the query “gift” shown in the character string TM1 is “gift”. Further, the search device 100 estimates that the query intention of the query “white” shown in the character string TM2 is “white object”.

Further, the search device 100 estimates the query intention of the query “gift white” shown in the character string TM13 based on the query intention of the query “gift” or “white” input immediately before the query intention.
Presumed to be a “gift”. For example, the search device 100 estimates the query intention of the query “gift white” based on the query intention of the query “gift” and its transition probability, and the query intention of “white” and its transition probability. In the example of FIG. 9, the search device 100 has a query intention “gift” of the query “gift” and its transition probability “0.9”, and a query intention “white object” of “white” and its transition probability “0.1”. ”And the query intention of the query“ gift white ”is estimated. presume.

  For example, the search device 100 estimates “0.9 * gift + 0.1 * white object” as the query intention of the query “gift white”. In this case, the search device 100 estimates that the query intention of the query “gift white” is 90% of the intention of the gift and 10% of the intention of searching for the white thing. As described above, in the example of FIG. 9, the search device 100 estimates the intention of a certain query based on the intention of the query input immediately before and the probability thereof.

  In the example of FIG. 9, the search device 100 estimates that the query intention of the query “purse gift” shown in the character string TM11 is “purse for gift”. Further, the search device 100 estimates that the query intention of the query “wallet white” shown in the character string TM12 is “white wallet”.

  In the example of FIG. 9, the search device 100 sets the query “purse gift white”, the query “purse gift white”, “purse white”, and “gift white” immediately before the query intention of the query “purse gift white” shown in the character string TM 21. Is estimated based on each query intention and each transition probability. The arrow “0.7” between the character string TM11 and the character string TM21 shown in FIG. 9 indicates the ratio that the query “purse gift” is input immediately before the query “purse gift white” calculated from the search log. Show. In addition, in the arrow “0.2” between the character string TM12 and the character string TM21 shown in FIG. 9, the query “purse white” is input immediately before the query “purse gift white” calculated from the search log. Indicates the percentage. Further, in the arrow “0.1” between the character string TM13 and the character string TM21 shown in FIG. 9, the query “gift white” is input immediately before the query “purse gift white” calculated from the search log. Indicates the percentage.

  For example, the search device 100 queries “0.7 * gift wallet + 0.2 * white wallet + 0.1 * (0.9 * gift + 0.1 * white object)” and query “wallet gift white”. Estimate as intention. In this case, for example, the search device 100 may estimate “looking for a white wallet as a gift wallet” as the query intention of the query “purse gift white”. Thus, the search device 100 can estimate the query intention more appropriately by estimating the intention of a certain query based on the intention and the probability of the query input immediately before the query. As described above, when there is a tail query of “wallet gift white”, the search device 100 assumes “wallet gift”, “wallet white”, which is assumed as a query (parent query) input immediately before this query. Based on "gift white" and the transition probability from these parent queries, the intention of the query "wallet gift white" is changed to the intention of the query entered immediately before by using a weighted average of the intention of each parent query. And the probability thereof.

[6. effect〕
As described above, the search device 100 according to the embodiment includes the acquisition unit 131 and the estimation unit 132. The acquisition unit 131 acquires a query used by the user who uses the terminal device 10 for the search. The estimation unit 132 estimates the intention of the query based on the user's behavior history before the user uses the query.

  Thereby, the search device 100 according to the embodiment can appropriately estimate the intention of the query input by the user based on the behavior history of the user before the user uses the query.

  In the search device 100 according to the embodiment, the estimation unit 132 estimates the intent of the query based on the input query input by the user before inputting the query.

  Thereby, the search device 100 according to the embodiment can appropriately estimate the intention of the query input by the user based on the input query input by the user before the input of the query.

  Further, in the search device 100 according to the embodiment, the estimation unit 132 is an input query input by the user before inputting a query including a plurality of character strings, and includes a part of character strings among the plurality of character strings. Estimate query intent based on the input query.

  Accordingly, the search device 100 according to the embodiment is based on an input query that is input by a user before inputting a query including a plurality of character strings and includes a part of character strings among the plurality of character strings. Thus, the intention of the query input by the user can be estimated appropriately.

  In the search device 100 according to the embodiment, the acquisition unit 131 acquires a query input at the end of a series of search actions performed by the user.

  Thereby, the search device 100 according to the embodiment can appropriately estimate the intent of the query even if the query input by the user is a tail query input at the end of a series of search actions performed by the user.

  Further, in the search device 100 according to the embodiment, the estimation unit 132 determines the intention of the query based on the transition probability between the queries that is assumed until the query input at the end of a series of search actions performed by the user. presume.

  Thereby, the search device 100 according to the embodiment can appropriately estimate the intention of the query (tail query) last input by the user.

  In the search device 100 according to the embodiment, the estimation unit 132 estimates whether the search based on the query is a search related to purchase as the query intention.

  Thereby, the search device 100 according to the embodiment can appropriately estimate the intention of the query input by the user by estimating whether the search by the query is a search related to the purchase as the intention of the query.

  In addition, the search device 100 according to the embodiment includes a providing unit 134. The providing unit 134 provides information related to the query when the estimation unit 132 estimates that the search based on the query is a search related to purchase.

  Thereby, when it is estimated that the search by the query is a search related to purchase, the search device 100 according to the embodiment appropriately provides information based on the estimated query intent of the user by providing information related to the query. Can be provided.

  In the search device 100 according to the embodiment, the estimation unit 132 estimates whether the search based on the query is a search related to a gift as the intention of the query.

  Thereby, the search device 100 according to the embodiment can appropriately estimate the intention of the query input by the user by estimating whether the search by the query is a search related to a gift as the intention of the query.

  Moreover, in the search device 100 according to the embodiment, when the estimation unit 132 can estimate the intention of the query based on the query, the estimation unit 132 estimates the intention of the query based on the query, and calculates the intention of the query based on the query. If it cannot be estimated, the intention of the query is estimated based on the user's behavior history before the user uses the query.

  Thereby, when the search device 100 according to the embodiment can estimate the intention of the query based on the query, the search device 100 can estimate the intention of the query based on the query, and can estimate the query intention based on the query. When the query cannot be performed, the intention of the query input by the user can be appropriately estimated by estimating the intention of the query based on the user's behavior history before the user uses the query.

[7. Hardware configuration)
The search device 100 according to the embodiment described above is realized by a computer 1000 having a configuration as shown in FIG. 10, for example. FIG. 10 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the search device. The computer 1000 includes a CPU 1100, RAM 1200, ROM 1300, HDD (Hard Disk Drive) 1400, communication interface (I / F) 1500, input / output interface (I / F) 1600, and media interface (I / F) 1700.

  The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400 and controls each unit. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, a program depending on the hardware of the computer 1000, and the like.

  The HDD 1400 stores programs executed by the CPU 1100, data used by the programs, and the like. The communication interface 1500 receives data from other devices via the network N and sends the data to the CPU 1100, and provides the data generated by the CPU 1100 to other devices via the network N.

  The CPU 1100 controls an output device such as a display and a printer and an input device such as a keyboard and a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. In addition, the CPU 1100 outputs the generated data to the output device via the input / output interface 1600.

  The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads such a program from the recording medium 1800 onto the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. Etc.

  For example, when the computer 1000 functions as the search device 100 according to the embodiment, the CPU 1100 of the computer 1000 implements the function of the control unit 130 by executing a program loaded on the RAM 1200. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them. However, as another example, these programs may be acquired from other devices via the network N.

  Although the embodiments of the present application have been described in detail with reference to the drawings, these are merely examples, and various modifications and improvements have been made based on the knowledge of those skilled in the art including the aspects described in the disclosure line of the invention. The present invention can be implemented in other forms.

[8. Others]
In addition, among the processes described in the above embodiment, all or part of the processes described as being automatically performed can be performed manually, or the processes described as being performed manually can be performed. All or a part can be automatically performed by a known method. In addition, the processing procedures, specific names, and information including various data and parameters shown in the document and drawings can be arbitrarily changed unless otherwise specified. For example, the various types of information illustrated in each drawing is not limited to the illustrated information.

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured.

  In addition, the processes described in the above-described embodiments can be appropriately combined within a range that does not contradict the processing contents.

  In addition, the “section (module, unit)” described above can be read as “means” or “circuit”. For example, the acquisition unit can be read as acquisition means or an acquisition circuit.

1 Estimating system 100 Searching device (estimating device)
REFERENCE SIGNS LIST 121 User information storage unit 122 Behavior information storage unit 123 Index information storage unit 124 Product information storage unit 130 Control unit 131 Acquisition unit 132 Estimation unit 133 Changing unit 134 Providing unit 10 Terminal device 50 Advertising distribution device N network

Claims (11)

An acquisition unit for acquiring a query used for a search by a user using a terminal device;
An estimation unit that estimates the intention of the query based on the user's behavior history before the user uses the query;
An estimation apparatus comprising: The estimation unit includes
The estimation device according to claim 1, wherein the intention of the query is estimated based on an input query input by the user before the input of the query. The estimation unit includes
The intent of the query is estimated based on an input query that is input by the user prior to the input of the query including a plurality of character strings and includes a part of the plurality of character strings. The estimation apparatus according to claim 2, wherein: The acquisition unit
The estimation device according to claim 1, wherein the query input at the end of a series of search actions performed by the user is acquired. The estimation unit includes
The estimation of the query according to claim 4, wherein the intent of the query is estimated based on a transition probability between the queries assumed until reaching the query input at the end of a series of search actions performed by the user. apparatus. The estimation unit includes
The estimation apparatus according to any one of claims 1 to 5, wherein as the intention of the query, it is estimated whether a search based on the query is a search related to purchase. A providing unit that provides information about the query when the estimation unit estimates that the search by the query is a search related to purchase;
The estimation apparatus according to claim 6, further comprising: The estimation unit includes
The estimation apparatus according to claim 5 or 7, wherein the intent of the query is to estimate whether a search based on the query is a search related to a gift. The estimation unit includes
If the intent of the query can be estimated based on the query, the intent of the query can be estimated based on the query, and the intent of the query cannot be estimated based on the query The estimation device according to claim 1, wherein the intention of the query is estimated based on an action history of the user before being used by the user. An estimation method performed by a computer,
An acquisition step of acquiring a query used for a search by a user using a terminal device;
An estimation step of estimating the intention of the query based on the user's behavior history before the user uses the query;
The estimation method characterized by including. An acquisition procedure for acquiring a query used for a search by a user using a terminal device;
An estimation procedure for estimating the intention of the query based on the user's behavior history before the user uses the query;
An estimation program for causing a computer to execute.

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