JP7368428B2 - Information processing device, information processing method, and information processing program - Google Patents

Description

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

2. Description of the Related Art Conventionally, techniques have been known that improve the efficiency of the supply of products or services (hereinafter collectively referred to as "transaction objects") by predicting user demand. As an example of such a technique, a technique is known in which a user who has an influence on other users is extracted and demand is predicted from the behavior of the extracted user.

Special table 2015-534180 publication

However, it cannot be said that the above-mentioned conventional technology predicts general trends. For example, in the conventional technology described above, demand is predicted based on the behavior of so-called influencers, and therefore it is not possible to estimate trends that influencers are not aware of.

The present application has been made in view of the above, and its purpose is to support the prediction of trends in transactions.

The information processing device according to the present application includes a receiving section and a generating section. The reception unit accepts selection of a category. The generation unit generates content including information indicating the number of searches and the growth rate of the number of searches for each search target corresponding to the category whose selection is accepted by the reception unit.

According to one aspect of the embodiment, it is possible to support the prediction of trends in transactions.

FIG. 1 is a diagram illustrating an example of information processing according to an embodiment. FIG. 2 is a diagram illustrating an example of the configuration of an information processing system including an information processing apparatus according to an embodiment. FIG. 3 is a diagram illustrating an example of the configuration of the information processing device according to the embodiment. FIG. 4 is a diagram illustrating an example of user information stored in the user information storage unit according to the embodiment. FIG. 5 is a diagram illustrating an example of search information stored in the search information storage unit according to the embodiment. FIG. 6 is a diagram illustrating an example of search target information stored in the search target information storage unit according to the embodiment. FIG. 7 is a diagram illustrating an example of content generated by the generation unit of the information processing device according to the embodiment. FIG. 8 is a diagram illustrating another example of content generated by the generation unit of the information processing device according to the embodiment. FIG. 9 is a diagram illustrating an example of display content displayed on the terminal device according to the embodiment. FIG. 10 is a flowchart illustrating an example of the flow of information processing by the processing unit of the information processing apparatus according to the embodiment. FIG. 11 is a flowchart illustrating an example of the flow of content provision processing by the processing unit of the information processing apparatus according to the embodiment. FIG. 12 is a flowchart illustrating an example of the flow of content update processing by the processing unit of the information processing apparatus according to the embodiment. FIG. 13 is a diagram illustrating still another example of content generated by the generation unit of the information processing device according to the embodiment. FIG. 14 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing device according to the embodiment.

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

[1. An example of information processing]
FIG. 1 is a diagram showing an example of information processing according to an embodiment, and in this embodiment, an information processing method is executed by an information processing system 100 including an information processing apparatus 1.

As shown in FIG. 1, the information processing system 100 includes an information processing device 1, terminal devices 2 ₁ to 2 _n , 3, and a search processing device 4. For example, n is an integer of 2 or more. The information processing device 1 is communicably connected _to each of the terminal devices _{2 1} to 2 _n , 3 and the search processing device 4 . Send and receive information.

Each of the users UA1 to UAn operates a corresponding one of the terminal devices 2 ₁ to 2 _n to access the search processing device 4 and search for content. The terminal devices 2 ₁ to 2 _n are, for example, smartphones, tablet PCs (Personal Computers), or notebook PCs. In the following, each of the users UA1 to UAn may be referred to as user UA when shown without being individually distinguished. Further, when each of the terminal devices 2 ₁ to 2 _n is shown without being individually distinguished, it may be referred to as the terminal device 2.

The user UB operates the terminal device 3, accesses the information processing device 1, and refers to the content provided by the information processing device 1 to predict trends in the transaction target such as products or services. . The terminal device 3 is, for example, a notebook PC or a desktop PC, but may also be a smartphone or a tablet PC.

The search processing device 4 is a device that searches for content such as web pages, websites, and/or image files existing on a network such as the Internet, and notifies the search results, and is realized by a cloud system or the like. . For example, the search processing device 4 provides search sites including portal sites and the like.

The search processing device 4 receives search queries sent from the terminal devices 2 ₁ to 2 _n (steps S1 ₁ to S1 _n ), and searches information related to the search target based on the information of the search target included in the received search query. A search process for searching for content is performed (steps S2 ₁ to S2 _n ).

Then, the search processing device 4 transmits the search results including a list of contents related to the search target to the terminal devices 2 ₁ to 2 _n (steps S3 ₁ to S3 _n ). Note that when the search query is a text search query, the information on the search target is information on a search keyword that indicates the search target in text.

The search keyword is one keyword or two or more keywords. If the search keyword is one keyword, the search target is indicated by one keyword, and if the search keyword is two or more keywords, the search target is indicated by one keyword. , indicated by two or more keywords. For example, when the search keyword is "X Beer", the search target is "X Beer", and when the search keyword is "Beer Company A", the search target is "Beer Company A".

When the search query is an image search query, the information to be searched is information on one keyword or two or more keywords corresponding to the search image, but may also be information on images corresponding to the search image. .

The search processing device 4 transmits the search information to the information processing device 1 (step S4), and the information processing device 1 collects the search information transmitted from the search processing device 4 (step S5). The search information includes, for example, information on the search target included in the search query of the terminal device 2, information on the date and time when the search query was sent, and the user ID of the user UA of the terminal device 2 that sent the search query. It will be done.

The user UB of the terminal device 3 can access the information processing device 1 and acquire content from the information processing device 1 in order to predict the trend of transaction objects in a desired category. Specifically, when the user UB operates the terminal device 3 to select a category for which the trend of the transaction target is to be predicted, category information, which is information on the selected category, is transmitted from the terminal device 3 to the search processing device 4. (Step S6).

The category selected by the user UB is a search target category, such as a product category or a service category, and is classified into, for example, a major category, a medium category, or a minor category. The user UB can, for example, operate the terminal device 3 to display a plurality of categories provided by the search processing device 4 on the terminal device 3, and select a desired category from among these plural categories. I can do it.

When the search processing device 4 acquires the category information from the terminal device 3, the search processing device 4 generates content indicating information indicating the number of searches and the growth rate of the number of searches for each search target corresponding to the selected category, which is the category selected by the user UB. Generate (step S7).

For example, the search processing device 4 generates content indicating information indicating the number of searches and the growth rate of the number of searches in a set period for each search target corresponding to the selected category. The set period is, for example, one year, three months, or one month, but may also be one week or one day. Further, the information indicating the growth rate of the number of searches is, for example, a growth rate score, which is a score that digitizes the growth rate of the number of searches in a set period, and has a minimum value of 0 and a maximum value of 1, for example.

For example, if the number of searches per unit period shorter than the set period is defined as the number of unit searches, the growth rate score is based on the search number setting change mode in which the change in the number of unit searches is the preset change mode of the number of searches. Indicates the degree of agreement. The search number setting change mode is, for example, a change mode in which the change in the rate of increase in the number of unit searches increases linearly, but it is a change mode in which the change in the rate of increase in the number of unit searches increases in a quadratic curve. Good too.

Further, the growth rate score may be, for example, a value obtained by weighting and summing the degree of coincidence of a change in the number of unit searches with a plurality of different search number setting change modes. In this case, the growth rate score becomes a larger value, for example, as the number of unit searches increases rapidly.

Then, the search processing device 4 provides the content generated in step S7 to the user UB by transmitting the content generated in step S7 to the terminal device 3 (step S8). Upon receiving the content from the search processing device 4, the terminal device 3 displays the received content (step S9).

In the example shown in FIG. 1, the content displayed on the terminal device 3 includes a graph plotted with a black circle for each search target, with the growth rate score on the vertical axis and the number of searches on the horizontal axis. In the graph shown in FIG. 1, search targets for which the number of searches is 100 or more and the growth rate score is 0.6 or more are plotted as black circles, indicating search targets with a high growth rate in the number of searches.

Further, in the content displayed on the terminal device 3, information to be searched is shown at a position corresponding to each black circle. In the example shown in FIG. 1, for example, "Beer of Company A", "Beer X", etc. are shown as information to be searched. Note that the information to be searched may be displayed by clicking the black circle of the search target.

In this way, since the growth rate score and the number of searches are shown for each search target in the content provided by the information processing device 1, the user UB can easily grasp the search targets for which the number of searches is increasing. can. Therefore, the user UB can easily predict the popularity of transaction objects in the selected category from the search objects whose number of searches is increasing.

In addition, search targets that have a low number of searches but a high growth rate score are search targets that correspond to transaction targets that are likely to become popular in the future, and from such search targets, user UB can detect signs of trends in transaction targets. It can be easily understood and the next trend of trading objects in the selected category can be easily predicted.

Additionally, a search target with a large number of searches and a high growth rate score corresponds to a transaction target that is already popular, and the higher the growth rate score, the more likely the future trend of the transaction target corresponding to such search target is. This also makes it easy to predict the popularity of the transaction object in the selected category, since there is a high possibility that it will continue or become more popular.

In this way, the information processing device 1 generates content indicating the number of searches and the growth rate of the number of searches for each search target corresponding to the selected category. Trends can be easily predicted.

Hereinafter, the configuration of the information processing device 1 that performs such processing and the information processing system 100 including the information processing device 1 will be described in detail.

[2. Configuration of information processing system 100]
FIG. 2 is a diagram illustrating an example of the configuration of an information processing system 100 including the information processing device 1 according to the embodiment. As shown in FIG. 2, the information processing system 100 according to the embodiment includes an information processing device 1, terminal devices 2 ₁ to 2 _n , 3, and a search processing device 4.

The information processing device 1 is capable of transmitting and receiving information to and from each of the terminal devices 2 and 3 and the search processing device 4 via the communication network 5. The communication network 5 is, for example, a WAN (Wide Area Network) such as the Internet or a LAN (Local Area Network). Note that the communication network 5 has a configuration including, for example, a mobile communication system such as 4G (4th Generation) or 5G (5th Generation), but is not limited to such an example.

[3. Configuration of information processing device 1]
Next, the configuration of the information processing device 1 according to the embodiment will be described using FIG. 3. FIG. 3 is a diagram showing an example of the configuration of the information processing device 1 according to the embodiment. As shown in FIG. 3, the information processing device 1 includes a communication section 10, a storage section 11, and a processing section 12. Note that the information processing device 1 includes an input section (for example, a keyboard, a mouse, etc.) that accepts various operations from an administrator using the information processing device 1, and a display section (for example, a liquid crystal display, etc.) for displaying various information. ).

[3.1. Communication department 10]
The communication unit 10 is realized by, for example, a NIC (Network Interface Card). The communication unit 10 is connected to the communication network 5 by wire or wirelessly, and transmits and receives information to and from each of the terminal devices 2 and 3 and the search processing device 4 via the communication network 5.

[3.2. Storage unit 11]
The storage unit 11 is realized by, for example, a semiconductor memory element 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 includes a user information storage unit 20, a search information storage unit 21, and a search target information storage unit 22.

[3.2.1. User information storage unit 20]
The user information storage unit 20 stores user information including information about the users UA1 to UAn of the terminal devices 2 ₁ to 2 _n . FIG. 4 is a diagram illustrating an example of user information stored in the user information storage unit 20 according to the embodiment.

As shown in FIG. 4, the user information stored in the user information storage unit 20 includes information such as "user ID (identifier)," "demographic attribute," and "psychographic attribute" for each user UA. "User ID" is identification information unique to each user UA.

"Demographic attribute" is information indicating the demographic attribute of the user UA. The demographic attributes are demographic attributes of the user UA, such as gender, age, address, occupation, or annual income. "Psychographic attribute" is information indicating the psychographic attribute of the user UA. Psychographic attributes are attributes that indicate values, lifestyle, personality, interests, etc. of the user UA.

In the example shown in FIG. 4, the user UA with the user ID "UA1" has the gender "male", the age "30s", the address "Minato-ku, Tokyo...", and the "clothes" ”, the interest level for “cars” is “3”, and the interest level for “travel” is “1”. Note that the higher the value of the interest level, the higher the level of interest. For example, the lowest value is 0 and the highest value is 5.

In addition, user UA with user ID "UA2" has a gender of "female," an age of "20s," an address of "Sapporo City, Hokkaido..." and a level of interest in "clothes." is "3", the level of interest in "cars" is "1", and the level of interest in "travel" is "5". In addition, user UA with user ID "UA3" has a gender of "male," an age of "40s," an address of "Fukuoka City, Fukuoka Prefecture..." and an interest in "clothes." The degree of interest is "0," the degree of interest in "cars" is "5," and the degree of interest in "travel" is "4."

[3.2.2. Search information storage unit 21]
The search information storage unit 21 stores search information transmitted from the search processing device 4. FIG. 5 is a diagram illustrating an example of search information stored in the search information storage unit 21 according to the embodiment.

As shown in FIG. 5, the search information stored in the search information storage unit 21 includes information such as "search query ID", "search target", "search date and time", and "search user ID" for each search query. include. "Search query ID" is identification information unique to each search query.

“Search target” is information to be searched. When the search query is a text search query, the search target information is information on a search keyword that indicates the search target in text. The search keyword is one keyword or two or more keywords. Further, when the search query is an image search query, the information to be searched is one keyword or two or more keywords corresponding to the search image, but it may also be information about an image corresponding to the search image. .

“Search date and time” is information indicating the date and time when the search query was received by the search processing device 4. “Search user ID” is the user ID of the user UA who caused the terminal device 2 to send the search query.

In the example shown in FIG. 5, the search query with the search query ID "Q1" has the search target "Beer of Company A", the search date and time "2021/9/15 8:02", and the search user ID It is "UA1". Further, in the search query with the search query ID "Q2", the search target is "X Beer", the search date and time is "2021/9/15 8:02", and the search user ID is "UA2".

Further, in the search query with the search query ID "Q3", the search target is "Toweta VXZ", the search date and time is "2021/9/15 8:03", and the search user ID is "UAn". Further, in the search query with the search query ID "Q4", the search target is "kiwi ice cream", the search date and time is "2021/9/15 8:03", and the search user ID is "UA1".

[3.2.3. Search target information storage unit 22]
The search target information storage unit 22 stores search target information that has been statistically processed by the processing unit 12. FIG. 6 is a diagram illustrating an example of search target information stored in the search target information storage unit 22 according to the embodiment.

As shown in FIG. 6, the search target information stored in the search target information storage unit 22 includes "search target", "number of searches", "growth rate score", "growth rate", and "attribute score". Contains information for each search target.

“Search target” is information to be searched. The “number of searches” is the number of searches in a set period, which is a period from which search targets are extracted. The set period is, for example, one year, three months, or one month.

The "growth rate score" is a score that quantifies the growth rate of the number of searches during the set period of the search target. For example, if the number of searches per unit period shorter than the set period is defined as the number of unit searches, the growth rate score is based on the search number setting change mode in which the change in the number of unit searches is the preset change mode of the number of searches. Indicates the degree of agreement.

The unit period is a period in which the set period is divided into multiple parts, and when the set period is one year, for example, 10 days, 20 days, or one month, and when the set period is three months, for example, Such as the 5th, 10th, or 15th. Note that the length of the unit period is not limited to the example described above, and may be constant regardless of the set period.

"Growth rate" is the number of searches in the current setting period relative to the number of searches Nr0 in the previous setting period, which is one setting period before the current setting period that includes the search date and time of the search query for which the number of searches is calculated. It is a ratio of the number Nr1, and is hereinafter referred to as elongation rate RR. For example, if the current setting period is from September 1, 2020 to August 31, 2021, the previous setting period was from September 1, 2019 to August 31, 2020. This is the period of In the following, the current setting period may be referred to as the current period, and the previous setting period may be referred to as the previous period. Further, the setting period one period before the previous setting period may be referred to as the period before the previous period.

The "attribute score" is a score indicating the gender ratio of target search users. The target search user is a user UA who has searched for a search target included in the search target information during a set period. That is, the target search user is the user UA that sent the search query used to generate the search target information. For example, when the attribute score is 0, the male-to-female ratio of the target search users is 1; the higher the score is than 0, the higher the female ratio of the target search users is; and the lower it is than 0, the male ratio of the target search users is 1. is high.

Note that the "attribute score" may be a score related to the attributes of the target search user, and is not limited to a score indicating the gender ratio of the search user. For example, the "attribute score" may be a score indicating how much higher or lower the average value of a specific attribute of a search target user is than the average value of a specific attribute of all search users, or It may also be a score indicating the degree of interest in a specific object.

For example, the "attribute score" may be a score indicating how much lower or higher the average age or average annual income of the target search user is than the average age or average annual income of all search users. In addition, the "attribute score" includes a score that indicates the degree of interest of the target search user in a specific object (for example, cars or clothes), a score that indicates how far the target search user's address is from Tokyo, It may also be a score that numerically indicates the age group with the largest number of search users.

In the example shown in FIG. 6, the search target for which the search keyword is "X Beer" has a search count of "25423", a growth rate score of "0.88", and a growth rate RR of "125.1". , and the attribute score is "-0.4". The search target for which the search keyword is "Toweta VXZ" has a search count of "84522", a growth rate score of "0.62", a growth rate RR of "62", and an attribute score of "-1". .6".

The search target for which the search keyword is "kiwi ice cream" has a search count of "3452", a growth rate score of "0.74", a growth rate RR of "2.1", and an attribute score of " 1.6". The search target for which the search keyword is "Company A beer" has a search count of "125477", a growth rate score of "0.12", a growth rate RR of "75", and an attribute score of " 1.9".

Although not shown in FIG. 6, the search target information includes "number of searches", "growth rate score", "growth rate", "attribute score", and "attribute score" for each attribute and search region of the target search user. "Search number ratio" etc. are included for each search target.

Further, the search target information storage unit 22 may store the above-mentioned search target information for each category. Further, the search target information storage unit 22 may store search target information for each attribute of the target search user, or for each category and each attribute of the target search user.

[3.3. Processing unit 12]
The processing unit 12 is a controller, and for example, processes various programs (information processing programs) stored in a storage device inside the information processing device 1 by a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). (corresponding to one example) is realized by being executed using RAM as a work area. Further, the processing unit 12 is, for example, a controller, and is realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

As shown in FIG. 3, the processing unit 12 includes a reception unit 30, an analysis unit 31, a generation unit 32, a provision unit 33, and a learning unit 34, and has information processing functions and operations described below. to realize or carry out. Note that the internal configuration of the processing unit 12 is not limited to the configuration shown in FIG. 3, and may be any other configuration as long as it performs information processing to be described later. Further, the connection relationship between the respective processing units included in the processing unit 12 is not limited to the connection relationship shown in FIG. 3, and may be other connection relationships.

[3.3.1. Reception Department 30]
The reception unit 30 receives search information transmitted from the search processing device 4 and adds the received search information to the search information stored in the search information storage unit 21.

Further, the reception unit 30 can also accept search information transmitted from a device other than the search processing device 4 and add the received search information to the search information stored in the search information storage unit 21. For example, the reception unit 30 can also receive search information indicating search results on a shopping site transmitted from a server device that provides the shopping site.

Further, the reception unit 30 accepts the selection of a category by the user UB. The category selected by the user UB is a search target category, such as a product category or a service category, and is classified into, for example, a major category, a medium category, or a minor category.

For example, when the major category is "home appliances," the intermediate categories are, for example, "refrigerator," "washing machine," "microwave oven," and "television." Also, if the major category is "home appliances" and the medium category is "washing machine," the minor category is, for example, "drum type washing machine" and "vertical washing machine." be.

Further, when the major category is "food", the intermediate categories are, for example, "cold drinks", "water", and "alcohol". In addition, if the major category is "Food" and the medium category is "Alcohol," the minor categories are "Wine," "Shochu," "Sake," "Beer," " These include "whiskey" and "Chinese liquor."

Further, the receiving unit 30 receives a content display request, an extraction range change request, etc. from the terminal device 3. The content display request is a request to send the content before changing the extraction range such as category, and the extraction range change request is a request to send the content after changing the extraction range such as category.

The reception unit 30 receives, for example, selection of a setting period, designation of a search area, change of the first threshold value N1, change of the second threshold value N2, etc. as an extraction range change request. The set period is a period for counting the number of searches included in the content, and is, for example, one year, six months, or three months. The number of searches and growth rate score are calculated based on search queries that include the search date and time in the aggregation period.

The search area is an area where the user UA operates the terminal device 2 to transmit a search query to the search processing device 4, and is specified, for example, in units of provinces, prefectures, or cities, towns, and villages. Doshu is a unit of Hokkaido, Tohoku region, Kanto region, Hokuriku region, Kinki region, etc. The area where the user UA operates the terminal device 2 to send a search query to the search processing device 4 is determined based on the user UA's address specified in the user information or the location information transmitted from the terminal device 2. .

The first threshold value N1 is a threshold value for the growth rate score of a search target to be included in the content provided to the user UB, and a search target having a growth rate score equal to or higher than the first threshold value N1 is applied to the content.

The second threshold N2 is a threshold for the growth rate of the number of searches for search targets included in the content provided to the user UB, and is the growth rate RR (= Search targets for which Nr1/Nr0) is greater than or equal to the second threshold value N2 are applicable to the content. For example, if the setting period is 1 year, the number of searches in the period from 1 year ago to 2 years ago is the number of searches Rr0, and the number of searches in the period from now to 1 year ago is the number of searches Rr1. .

[3.3.2. Analysis section 31]
The analysis unit 31 performs statistical processing on the search information stored in the search information storage unit 21 , generates search target information, and stores the generated search target information in the search target information storage unit 22 .

The analysis unit 31 calculates the number of searches, growth rate score, growth rate RR, and attribute score for each search target in the attribute unit and search region unit of the target search user, and calculates the number of searches, growth rate score for each search target. , growth rate RR, and attribute score are stored in the search target information storage unit 22. The attributes of the target search user include, for example, gender, age, area of residence, occupation, annual income, and interests. Further, the search area unit is, for example, an area of Hokkaido, a prefecture, or a city, town, or village.

Note that the analysis unit 31 generates search target information at a preset period, but when the reception unit 30 receives selection of a category, selection of a set period, selection of a search area, etc., the reception unit 30 generates search target information. It is also possible to generate search target information according to selections or changes accepted by.

[3.3.3. Generation unit 32]
The generation unit 32 calculates the number of searches and the number of searches for each search target corresponding to the selected category, based on the selected category whose selection is accepted by the reception unit 30 and the search target information generated by the analysis unit 31. Generate content including information indicating the growth rate.

The search target corresponding to the selected category is a search target in a category that matches or is similar to the selected category. For example, if the selected category is "beer", a search that includes the character string "beer" or "happoshu" is performed. The search target includes a target, a search target that includes a product name of beer, a search target that is presumed to be related to beer, etc., but is not limited to such examples.

Further, the generation unit 32 can generate graph content containing information indicating the number of searches and growth rate score of each search target corresponding to the selected category, with the number of searches as the horizontal axis and the growth rate score as the vertical axis. . Note that although the horizontal axis is expressed logarithmically, it may be expressed non-logarithmically. Furthermore, although the vertical axis is expressed as a non-logarithm, it may be expressed as a logarithm.

FIG. 7 is a diagram illustrating an example of content generated by the generation unit 32 of the information processing device 1 according to the embodiment. As shown in FIG. 7, the content 50 generated by the generation unit 32 is graph content that includes information indicating the number of searches and growth rate scores for each search target corresponding to the selected category "Food - Alcohol - Beer". be. In the selected category "Food-Alcohol-Beer", the major category is "Food", the medium category is "Alcohol", and the minor category is "Beer".

In the content 50 shown in FIG. 7, each search target is indicated by a black circle, and information about the search target is added at a position corresponding to each black circle. In the example shown in FIG. 7, the information to be searched includes "Namataru Kira", "Happoshu Popular", "Z Beer", "Awawa Beer", "Geki Awa Beer", "Togarashi Beer", "Company A" beer," "X beer," "dry beer," or "draft beer."

Further, the generation unit 32 can also generate, as the content 50, graph content that includes information indicating the search target in a manner according to the attribute score. FIG. 8 is a diagram illustrating another example of content 50 generated by the generation unit 32 of the information processing device 1 according to the embodiment. As shown in FIG. 8, in the content 50 generated by the generation unit 32, the search target is indicated by a circle filled with a color corresponding to the attribute score. In FIG. 8, for convenience, differences in color are shown as differences in shading.

In the content 50 shown in FIG. 8, since each search target is shown in a manner according to the attribute score, the user UB can easily understand the attribute score of the search target. For example, when the attribute score indicates the gender ratio of target search users, it is possible to easily understand which of the men and women performs more searches.

Returning to FIG. 3, the description of the generation unit 32 will be continued. The generation unit 32 includes an extraction processing unit 40 and a generation processing unit 41. The extraction processing unit 40 extracts a search target corresponding to the selected category as an extraction search target. Extraction of the search target is performed by extracting the search target corresponding to the selected category from among the plurality of search targets indicated by the search target information stored in the search target information storage unit 22.

The extraction processing unit 40 uses, for example, a category estimation model, which is a model generated by machine learning and which estimates a search target category from search target information, to extract and search the search target corresponding to the selected category. It can be extracted as a target.

The category estimation model is generated, for example, by machine learning using a neural network such as a convolutional neural network or a regression neural network, but is not limited to such examples. For example, the category estimation model may be generated using machine learning using a learning algorithm such as linear regression or logistic regression instead of a neural network.

Further, when the storage unit 11 includes category information that associates each search target with a category, the extraction processing unit 40 extracts the search target corresponding to the selected category as an extraction search target based on the category information. can do.

For example, when no category is selected, the extraction processing unit 40 extracts, as extraction search targets, search targets whose growth rate score is equal to or higher than the first threshold value N1 from among the search targets searched in the set period. Further, when a category is selected, the extraction processing unit 40 extracts, as extraction search targets, search targets whose growth rate score is equal to or higher than the first threshold value N1 among the search targets that have been searched in the set period and correspond to the selected category. .

The first threshold value N1 is, for example, 0.6 as an initial value. As a result, search targets that are gaining attention can be extracted as search targets. Note that the first threshold value N1 is a fixed value, but may be changeable by an operation on the terminal device 3 by the user UB.

Further, the extraction processing unit 40 further narrows down and extracts search targets for which the growth rate RR, which is the ratio of the growth rate of the current period to the growth rate of the number of searches in the previous period, is equal to or higher than the second threshold N2. . This growth rate is, for example, the average value of growth rates per unit period in each of the previous period and the current period.

The second threshold value N2 is, for example, 50 as an initial value. Thereby, the extraction processing unit 40 extracts search targets that have a period of high growth rate for each set period, such as a search target corresponding to seasonal products or services whose sales are particularly high in a certain season. Can be excluded from search targets.

Therefore, for example, search targets whose growth rate in the number of searches increases at a certain time of the year can be excluded from the extracted search targets. The second threshold N2 can be changed by the user UB's operation on the terminal device 3, but may be a fixed value.

In addition, instead of the growth rate RR, the extraction processing unit 40 extracts search targets for which the ratio of the growth rate of the number of searches in the current period to the growth rate of the number of searches in the previous period is equal to or higher than the second threshold N2. It is also possible to narrow down the selection further.

This also makes it possible to exclude, from the extracted search targets, search targets for which the rate of increase in the number of searches increases every year during a certain period of the year. The second threshold N2 can be changed by the user UB's operation on the terminal device 3, but may be a fixed value.

Further, the extraction processing unit 40 may further narrow down the search targets searched by the target search user having the attributes whose selection has been accepted by the reception unit 30 among the search targets corresponding to the selected category, and extract them as extraction search targets. can.

For example, when a search area that is the area of the target search user is selected, the extraction processing unit 40 extracts and searches the search targets searched by the target search user in the selected search area from among the search targets corresponding to the selected category. The target can be further narrowed down and extracted.

In addition, when the age group of the target search user is selected, the extraction processing unit 40 further extracts the search targets searched by the target search user in the selected age group from among the search targets corresponding to the selected category. Can be narrowed down and extracted.

In addition, when a car is selected as an interest of the target search user, the extraction processing unit 40 extracts a search result that is searched by the search target search user who has a high level of interest in the selected car among the search targets corresponding to the selected category. The target can be further narrowed down and extracted as an extraction search target.

In addition, when a convenience store is selected as an interest of the target search user, the extraction processing unit 40 determines whether the target search user has a high degree of interest in the selected convenience store among the search targets corresponding to the selected category. The search target can be further narrowed down and extracted as an extraction search target.

The generation processing unit 41 generates information including information indicating the number of searches and the growth rate score of the extracted search target extracted by the extraction processing unit 40 as the content 50. For example, the generation processing unit 41 generates graph content as the content 50, which includes information indicating the number of searches and the growth rate score of each search target corresponding to the selected category, with the number of searches on the horizontal axis and the growth rate score on the vertical axis.

[3.3.4. Providing Department 33]
The providing unit 33 provides the display content including the content 50 to the user UB by transmitting the display content including the content 50 generated by the generating unit 32 to the terminal device 3 via the communication unit 10 and the communication network 5. .

FIG. 9 is a diagram showing an example of display content displayed on the terminal device 3 according to the embodiment. As shown in FIG. 9, the display content 60 displayed on the terminal device 3 includes the content 50 generated by the generation unit 32, a category selection area 51, a growth rate selection area 52, a setting period selection area 53, It includes an attribute selection area 54 and an attribute score guide area 55.

The content 50 is the same as the content 50 shown in FIG. 8, and the horizontal axis is the number of searches and the vertical axis is the growth rate score.The content 50 is the same as the content 50 shown in FIG. Contains information indicating the rate score.

The category selection area 51 is an area for selecting a category, and includes pull-down menus 51a, 51b, and 51c for each of a major category, a medium category, and a minor category. By operating the terminal device 3, the user UB can select a major category, a medium category, and a minor category by operating the pull-down menus 51a, 51b, and 51c. Note that in the category selection area 51, instead of the pull-down menus 51a, 51b, and 51c, an input frame may be provided so that a category can be selected by inputting a keyword.

The elongation rate selection area 52 is an area for changing the second threshold value N2, and the user UB can change the second threshold value N2 by operating the terminal device 3 and moving the slide bar 52a. can. The second threshold value N2 is a threshold value for the growth rate RR, and is a period in which the growth rate increases in each set period, such as a search target corresponding to a seasonal product or service that attracts attention or sales are particularly high in a certain season. It is possible to exclude certain search targets from the extraction search targets.

When the slide bar 52a is moved, the generation unit 32 updates the content 50. Specifically, the generation unit 32 extracts search targets whose growth rate RR is equal to or higher than the second threshold N2 from among the search targets corresponding to the selected category, and calculates the number of searches and growth rate score of the number of searches for each extracted search target. The content 50 including information indicating the above is regenerated. The providing unit 33 provides the regenerated content 50 to the user UB.

The setting period selection area 53 is an area for selecting a setting period, and includes a radio button for selecting 1 year as the setting period, a radio button for selecting 6 months as the setting period, and a radio button for selecting 6 months as the setting period. and a radio button for selecting 3 months. The user UB can select a setting period by operating the terminal device 3 and selecting one of the radio buttons.

The attribute selection area 54 is an area for selecting the attribute of the target search user, and in the example shown in FIG. 9, a check box for selecting the region of the target search user is shown. The user UB can select the region of the target search user by operating the terminal device 3 and selecting one of the check buttons. Note that the attribute selection area 54 may allow selection of the target search user's gender, age group, occupation, annual income, interests, etc., instead of or in addition to the target search user's region, as attributes of the target search user.

The attribute score guide area 55 is an area that indicates a color according to the attribute score of the display target plotted as a circle in the content 50. In the content 50, the search target is indicated as a circle filled with a color according to the attribute score. has been done. In FIG. 9, for convenience, differences in color are shown as differences in shading.

[3.3.5. Learning Department 34]
Returning to FIG. 3, the description of the processing unit 12 will be continued. The learning unit 34 generates a category estimation model by machine learning. For example, the learning unit 34 can generate a category estimation model using a data set that includes search target information and categories. In a dataset, categories are used as labels.

For example, in the content 50, when the user UB selects that the search target is not the selected category but another category, the data set of the information on the search target and the other category is created by the learning unit 34. used.

[4. Processing procedure]
Next, a procedure of information processing by the processing unit 12 of the information processing device 1 according to the embodiment will be described. FIG. 10 is a flowchart illustrating an example of the flow of information processing by the processing unit 12 of the information processing device 1 according to the embodiment.

As shown in FIG. 10, the processing unit 12 of the information processing device 1 determines whether a content display request has been received from the terminal device 3 (step S10). If the processing unit 12 determines that a content display request has been received (step S10: Yes), it performs content provision processing (step S11). The process in step S11 is the process in steps S20 to S22 shown in FIG. 11, and will be described in detail later.

When the process of step S11 is completed or when it is determined that the content display request has not been received (step S10: No), the processing unit 12 determines whether or not an extraction range change request has been received from the terminal device 3. (Step S12). If the processing unit 12 determines that the extraction range change request has been received (step S12: Yes), it performs a content update process (step S13). The process in step S13 is the process in steps S30 to S41 shown in FIG. 12, and will be described in detail later.

When the processing in step S13 is completed, or when it is determined that the extraction range change request has not been received (step S12: No), the processing unit 12 determines whether or not it is the timing to end the operation (step S14). ). For example, when the power of the information processing device 1 is turned off, or when it is determined that a termination operation has been performed by operating an operation portion (not shown) of the information processing device 1, the processing unit 12 determines the operation termination timing. It is determined that it has become.

If the processing unit 12 determines that the operation end timing has not arrived (step S14: No), the process proceeds to step S10, and if it determines that the operation end timing has arrived (step S14: Yes), the process proceeds to step S10. The indicated process ends.

FIG. 11 is a flowchart illustrating an example of the flow of content provision processing by the processing unit 12 of the information processing device 1 according to the embodiment. As shown in FIG. 11, the processing unit 12 acquires or generates search target information based on the search information (step S20).

Next, the processing unit 12 generates content 50 indicating the number of searches and growth rate score for each search target based on the search target information acquired or generated in step S20 (step S21). The search targets for which the number of searches and the growth rate score are shown in the content 50 are, for example, searches for which the growth rate score is at least the first threshold N1 and the growth rate RR is at least the second threshold N2 among the search targets searched in the set period. It is a target.

Next, the processing unit 12 transmits the display content 60 including the generated content 50 to the terminal device 3 to provide the display content 60 to the user UB (step S22), and ends the process shown in FIG. 11.

FIG. 12 is a flowchart illustrating an example of the flow of content update processing by the processing unit 12 of the information processing device 1 according to the embodiment. As shown in FIG. 12, the processing unit 12 determines whether the extraction range change request is a category selection request (step S30).

If the processing unit 12 determines that the extraction range change request is a category selection request (step S30: Yes), it extracts a search target corresponding to the selected category indicated by the selection request (step S31). If the processing unit 12 determines that the extraction range change is not a category selection request (step S30: No), it determines whether the extraction range change is a search area selection request (step S32).

When the processing unit 12 determines that the extraction range change request is a search area selection request (step S32: Yes), the processing unit 12 extracts a search target corresponding to the selected search area indicated by the selection request (step S33). . If the processing unit 12 determines that the extraction range change is not a request to select a search area (step S33: No), the processing unit 12 determines whether the extraction range change is a request to select an attribute of the search user (step S34). .

If the processing unit 12 determines that the extraction range change is a request to select the search user's attribute (step S34: Yes), the processing unit 12 extracts the search target corresponding to the selected search user attribute indicated in the selection request ( Step S35). If the processing unit 12 determines that the extraction range change is not a request to select an attribute of the search user (step S34: No), the processing unit 12 determines whether the extraction range change is a request to change the first threshold N1 (step S34: No). S36).

If the processing unit 12 determines that the extraction range change is a request to change the first threshold value N1 (step S36: Yes), the processing unit 12 selects search targets that have a growth rate score equal to or higher than the changed first threshold value N1 indicated by the change request. is extracted (step S37). When determining that the extraction range change is not a request to change the first threshold N1 (step S36: No), the processing unit 12 determines whether the extraction range change is a request to change the second threshold N2 (step S36: No). S38).

If the processing unit 12 determines that the extraction range change is a request to change the second threshold N2 (step S38: Yes), the processing unit 12 selects a search target that has a growth rate RR equal to or higher than the changed second threshold N2 indicated by the change request. is extracted (step S39).

The processing unit 12 determines that when the process of step S31 is finished, when the process of step S33 is finished, when the process of step S35 is finished, when the process of step S37 is finished, when the process of step S39 is finished, Alternatively, if it is determined that the extraction range change is not a request to change the second threshold N2 (step S38: No), content 50 indicating the number of searches and growth rate score for each extracted search target is generated (step S40).

Then, the processing unit 12 transmits the display content 60 including the content 50 generated in step S40 to the terminal device 3, thereby providing the display content 60 to the user UB (step S41), and ends the process shown in FIG. do.

[5. Modified example]
In the example described above, the generation unit 32 generated the content 50 indicating the number of searches and the growth rate score, but it can also generate the content 50 indicating the purchase growth rate score instead of the number of searches. The purchase growth rate score indicates the number of purchases and the growth rate of the number of purchases of the transaction object corresponding to the search target.

In this case, purchase information indicating the number of purchases of the transaction object corresponding to the search object is stored in the storage section 11, and the generation section 32 generates the transaction information corresponding to the search object based on the purchase information stored in the storage section 11. Content 50 indicating the number of purchases of the target and the purchase growth rate score is generated. The content 50 generated by the generation unit 32 is provided to the user UB by the providing unit 33.

Further, the reception unit 30 can also receive a third threshold N3 that is a threshold for the number of purchases. In this case, the extraction processing unit 40 of the generation unit 32 extracts search targets for which the number of purchases is equal to or higher than the third threshold N3 from among the search targets corresponding to the selected category, and the number of searches and growth in the number of searches for each extracted search target. Content 50 can be generated that includes information indicating a rate score.

The analysis unit 31 can also generate search target information from a search query for one or more sites specified by the user UB among a plurality of sites including search sites and shopping sites. Thereby, the processing unit 12 of the information processing device 1 can generate the content 50 by narrowing down the search content of the user UA searching for a specific site, and can generate the next popular transaction target for each site or group of sites. It is possible to easily grasp the search targets corresponding to the current trend or the search targets corresponding to the transaction targets that continue to be popular. The search object corresponding to the transaction object is a search object that searches for the transaction object, and the transaction object is specified by the information on the search object.

The extraction processing unit 40 of the generation unit 32 determines the search target corresponding to the transaction target predicted to become popular next, in other words, the search target corresponding to the transaction target predicted to become popular next, and determines the search target corresponding to the transaction target predicted to become popular next. Then, search targets corresponding to the predicted transaction target can be automatically extracted. In this case, the generation processing unit 41 can include in the content 50 or the display content 60 a next trending list, which is a list of search targets corresponding to transaction targets predicted by the extraction processing unit 40 to become popular.

Thereby, the user UB can quickly grasp, for example, the transaction target that is predicted to become popular next. The transaction object predicted to become popular next is the transaction object corresponding to the search object for which the number of searches in the current period is below the fourth threshold and the growth rate score is above the fifth threshold. For example, in the example shown in FIG. 9, search targets predicted to become popular next are search targets for which the number of searches is 1000 or less and the growth rate score is 0.6 or more.

In addition, the extraction processing unit 40 determines, in the content 50, the search target range corresponding to the transaction target that is predicted to become popular next, and the transaction target that is currently trending and is expected to continue or become more popular in the future. It is also possible to specify the search target range corresponding to the search target range. FIG. 13 is a diagram illustrating still another example of content 50 generated by the generation unit 32 of the information processing device 1 according to the embodiment.

In the example shown in Figure 13, the area where the next epidemic is expected to occur (hereinafter sometimes referred to as the next break area) and the area where the epidemic is currently occurring and where it is predicted that the epidemic will continue or further epidemics ( (hereinafter sometimes referred to as break areas) are shown in different colors. In FIG. 13, differences in color are indicated by hatching.

The next break area and break area are not limited to the example shown in FIG. 13. For example, a range where the number of searches is 500 to 3000 and the growth rate score is 0.8 or more can be set as the next break area. The next break area and the break area can be set by the user UB, but the learning unit 34 sets the next break area and the break area for each category based on the number of searches and growth rate score of the search target corresponding to the transaction target that was actually popular in the past. The next break area and break area can also be set for each attribute of the target search user and for each set period.

Further, in the example described above, the search target information stored in the search target information storage unit 22 includes the growth rate RR, but is not limited to this example. For example, the search target information storage unit 22 may store search target information for the current period, search target information for the previous period, and the like for each set period. In this case, the search target information for each period includes, for example, the "number of searches," "growth rate score," and "attribute score" for each attribute of the search user for each search target. The extraction processing unit 40 of the processing unit 12 can calculate the growth rate RR based on the “number of searches” included in the search target information for the previous period and the current period.

Further, in the above example, the growth rate score is used as an example of information indicating the growth rate of the number of searches, but the information indicating the growth rate of the number of searches is not limited to the growth rate score. For example, the analysis unit 31 classifies the aspect of the growth rate of the number of searches into a plurality of aspects, and replaces the information indicating the closest aspect among these multiple aspects with the growth rate score to indicate the growth rate of the number of searches. It can also be information. Note that the plurality of aspects is, for example, represented by a combination of straight lines or curved lines, such as a plurality of straight lines with different slopes, quadratic curves with different coefficients and positive/negative values, sine waves or rectangular waves with different sizes and periods, or these. It is expressed as a combination.

Furthermore, in the above example, the content 50 is a graph content in which the number of searches is the horizontal axis and the growth rate score is the vertical axis, but the content 50 is a graph content in which the number of searches is the vertical axis and the growth rate score is the horizontal axis. It may be content.

[6. Hardware configuration]
The information processing device 1 or the terminal devices 2 and 3 according to the embodiments described above is realized by, for example, a computer 80 having a configuration as shown in FIG. 14. The information processing device 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 device 1 according to the embodiment. The computer 80 includes a CPU 81, a RAM 82, a ROM (Read Only Memory) 83, an 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 a program stored in the ROM 83 or HDD 84 and controls each part. The ROM 83 stores a boot program executed by the CPU 81 when the computer 80 is started, programs depending on the hardware of the computer 80, and the like.

The HDD 84 stores programs executed by the CPU 81 and data used by the programs. The communication interface 85 receives data from other devices via the communication network 5 (see FIG. 2) and sends it to the CPU 81, and sends data generated by the CPU 81 to the other devices via the communication network 5.

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

Media interface 87 reads programs or data stored in recording medium 88 and provides them to CPU 81 via RAM 82. The CPU 81 loads this 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 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 80 functions as the information processing device 1 according to the embodiment, the CPU 81 of the computer 80 realizes the functions of the processing unit 12 by executing a program loaded onto the RAM 82. Furthermore, 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 from another device via the communication network 5.

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

Furthermore, each component of each device shown in the drawings is functionally conceptual, and does not necessarily need to be physically configured as shown in the drawings. In other words, the specific form of distributing and integrating each device is not limited to what is shown in the diagram, and all or part of the devices can be functionally or physically distributed or integrated in arbitrary units depending on various loads and usage conditions. Can be integrated and configured. For example, at least two or more of the receiving section 30, the analyzing section 31, the generating section 32, the providing section 33, and the learning section 34 shown in FIG. 3 may be integrated. Further, for example, the information stored in the storage unit 11 may be stored in a predetermined external storage device via the communication network 5.

Further, the information processing device 1 may be configured with two or more server devices, and in this case, the information processing device 1 has a configuration including, for example, a processing server and a storage server. Furthermore, the embodiments and modifications described above can be combined as appropriate within a range that does not conflict with the processing contents.

[8. effect〕
As described above, the information processing device 1 according to the embodiment includes the receiving section 30 and the generating section 32. The reception unit 30 accepts selection of a category. The generation unit 32 generates content 50 that includes information indicating the number of searches and the growth rate of the number of searches for each search target corresponding to the category whose selection is accepted by the reception unit 30 . Thereby, the information processing device 1 can generate the content 50 that allows the user UB to easily understand the search targets for which the number of searches is increasing in the selected category, and the user UB can easily understand the search targets for which the number of searches is increasing in the selected category. From the search target, it is possible to easily predict the popularity of the transaction target in the selected category.

The generation unit 32 also generates the number of searches and searches for each search target corresponding to the category whose selection has been accepted by the reception unit 30, with the number of searches on one axis of the horizontal axis and the vertical axis and the growth rate on the other axis. Graph content including information indicating the growth rate of numbers is generated as content 50. As a result, the information processing device 1 selects a search target corresponding to a transaction target that is predicted to become popular next, and a search target corresponding to a transaction target that is currently trending and is expected to continue or become more popular in the future. User UB can be made aware of this.

Further, the generation unit 32 includes an extraction processing unit 40 and a generation processing unit 41. The extraction processing unit 40 extracts search targets whose growth rate score, which is a score that quantifies the growth rate of the number of searches, is equal to or higher than a first threshold value N1 from among the search targets corresponding to the category whose selection is accepted by the reception unit 30. Extract as a search target. The generation processing unit 41 generates information including information indicating the number of searches for the extracted search target and the growth rate of the number of searches as the content 50. As a result, the information processing device 1 creates content 50 by narrowing down the search targets to search targets that are expected to become popular next or to transaction targets that are currently trending and are predicted to continue or become more popular in the future. can do. Therefore, the user UB can easily grasp search targets that are expected to become popular next and search targets corresponding to transaction targets that are currently trending and are expected to continue or become more popular in the future.

Further, the number of searches is the number of searches per set period, which is a set period. The extraction processing unit 40 extracts, as extraction search targets, search targets for which the ratio of the number of searches in the set period to the number of searches in the period before the set period is equal to or greater than the second threshold value N2. Thereby, the information processing device 1 extracts search targets that have a period of high growth rate for each set period, such as a search target corresponding to seasonal products or services whose sales are particularly high in a certain season. Can be excluded from search targets. Therefore, the information processing device 1 can exclude, for example, a search target whose growth rate in the number of searches increases at a certain time of the year from the extracted search targets.

Further, the number of searches is the number of searches per set period, which is a set period. The extraction processing unit 40 extracts search targets for which the growth rate ratio, which is the ratio of the growth rate of the number of searches in the set period to the growth rate of the number of searches in the period before the set period, is equal to or higher than the second threshold N2. Extract as. As a result, the information processing device 1 can search for search targets that have a period of high growth rate for each set period, such as a search target that corresponds to a seasonal product or service whose sales are particularly high in a certain season. Can be excluded from extraction search targets. Therefore, the information processing device 1 can exclude, for example, a search target whose growth rate in the number of searches increases at a certain time of the year from the extracted search targets.

Further, the accepting unit 30 accepts the setting of the second threshold value N2. The extraction processing unit 40 extracts the extraction search target based on the second threshold N2 whose setting is accepted by the reception unit 30. Thereby, the user UB can easily narrow down the extraction search targets.

Further, the accepting unit 30 accepts the setting of the first threshold value N1. The extraction processing unit 40 extracts an extraction search target based on the first threshold N1 whose setting is accepted by the reception unit 30. Thereby, the user UB can easily narrow down the extraction search targets.

Further, the receiving unit 30 receives a selection of an attribute of a target search user, who is a search user who has searched for a search target. The extraction processing unit 40 extracts, as an extraction search target, a search target searched by a target search user having an attribute whose selection was accepted by the reception unit 30 from among the search targets corresponding to the category whose selection was accepted by the reception unit 30. do. As a result, search targets that correspond to transaction targets that are predicted to become the next trend within the attributes of user UA targeted by user UB, and transaction targets that are currently trending and are predicted to continue or become more popular in the future. You can easily understand what you are searching for.

In addition, the extraction processing unit 40 uses a model that is generated by machine learning to estimate the search target category from the search target information, and selects a search target corresponding to the category whose selection is accepted by the reception unit 30. Extract as the extraction search target. Thereby, the information processing device 1 can easily extract the search target corresponding to the selected category.

Although the embodiments of the present application have been described above in detail based on the drawings, this is merely an example, and various modifications and improvements can be made based on the knowledge of those skilled in the art, including the embodiments described in the disclosure section of the invention. It is possible to implement the invention in other forms.

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

1 Information processing device 2, _{2 1} to 2 _n , 3 Terminal device 4 Search processing device 5 Communication network 10 Communication unit 11 Storage unit 12 Processing unit 20 User information storage unit 21 Search information storage unit 22 Search target information storage unit 30 Reception unit 31 Analysis unit 32 Generation unit 33 Providing unit 34 Learning unit 40 Extraction processing unit 41 Generation processing unit 50 Content 60 Display content 100 Information processing system UA, UA1 to UAn, UB User

Claims (12)

A reception department that accepts category selections,
a generation unit that generates content including information indicating the number of searches for each search target corresponding to the category whose selection is accepted by the reception unit and the growth rate of the number of searches ;
The generation unit is
Information indicating the number of searches and the growth rate for each search target corresponding to the category for which the selection has been accepted, with the number of searches as one axis of the horizontal axis and the vertical axis and the growth rate as the other axis. Generate graph content as the content,
In the graph content, each of the search targets is shown in a manner according to an attribute score that is a score related to the attributes of the search user.
An information processing device characterized by: A reception department that accepts category selections,
a generation unit that generates content including information indicating the number of searches for each search target corresponding to the category whose selection is accepted by the reception unit and the growth rate of the number of searches;
The number of searches is
is the number of searches per set period, which is the set period,
The generation unit is
The number of searches for search targets that have a growth rate score, which is a score that quantifies the growth rate, of the search targets corresponding to the category for which the selection has been accepted and that are equal to or higher than a first threshold, in a period earlier than the set period. an extraction processing unit that extracts search targets for which the ratio of the number of searches in the set period to
a generation processing unit that generates information including information indicating the number of searches and the growth rate of the extraction search target as the content;
The reception department is
accepting the setting of the second threshold;
The extraction processing unit is
The information processing apparatus is characterized in that the extraction search target is extracted based on the second threshold value whose setting is accepted by the reception unit . A reception department that accepts category selections,
a generation unit that generates content including information indicating the number of searches for each search target corresponding to the category whose selection is accepted by the reception unit and the growth rate of the number of searches;
The generation unit is
an extraction processing unit that extracts, as search targets, search targets whose growth rate score, which is a score obtained by quantifying the growth rate, is equal to or higher than a first threshold among the search targets corresponding to the category for which the selection has been accepted;
a generation processing unit that generates information including information indicating the number of searches and the growth rate of the extraction search target as the content;
The reception department is
accepting the setting of the first threshold;
The extraction processing unit is
Extracting the extraction search target based on the first threshold whose setting is accepted by the reception unit.
An information processing device characterized by: The generation unit is
Information indicating the number of searches and the growth rate for each search target corresponding to the category for which the selection has been accepted, with the number of searches as one axis of the horizontal axis and the vertical axis and the growth rate as the other axis. The information processing apparatus according to claim 2 or 3, wherein graph content is generated as the content. The reception department is
Accepting the selection of attributes of the search user who performed the search for the search target,
The extraction processing unit is
2. A search target searched by a search user having an attribute whose selection has been accepted by the reception unit among the search targets corresponding to the category for which the selection has been accepted is extracted as the extraction search target. - The information processing device described in any one of 4 . The extraction processing unit is
Using a model that is generated by machine learning and that estimates the category of the search target from the information of the search target, the search target corresponding to the category whose selection is accepted by the reception unit is extracted as the search target. 6. The information processing apparatus according to claim 2 , wherein the information processing apparatus extracts the information. An information processing method performed by a computer, the method comprising:
a reception process for accepting category selection;
a generation step of generating content including information indicating the number of searches for each search target corresponding to the category whose selection was accepted in the reception step and the growth rate of the number of searches ;
The production step includes:
Information indicating the number of searches and the growth rate for each search target corresponding to the category for which the selection has been accepted, with the number of searches as one axis of the horizontal axis and the vertical axis and the growth rate as the other axis. Generate graph content as the content,
In the graph content, each of the search targets is shown in a manner according to an attribute score that is a score related to the attributes of the search user.
An information processing method characterized by: An information processing method performed by a computer, the method comprising:
a reception process for accepting category selection;
a generation step of generating content including information indicating the number of searches for each search target corresponding to the category whose selection was accepted in the reception step and the growth rate of the number of searches ;
The number of searches is
is the number of searches per set period, which is the set period,
The production step includes:
The number of searches for search targets that have a growth rate score, which is a score that quantifies the growth rate, of the search targets corresponding to the category for which the selection has been accepted and that are equal to or higher than a first threshold, in a period earlier than the set period. an extraction processing step of extracting search targets for which the ratio of the number of searches in the set period to
a generation processing step of generating information including information indicating the number of searches and the growth rate of the extraction search target as the content,
The reception process includes:
accepting the setting of the second threshold;
The extraction processing step is
Extracting the extraction search target based on the second threshold whose setting is accepted in the accepting step.
An information processing method characterized by: An information processing method performed by a computer, the method comprising:
a reception process for accepting category selection;
a generation step of generating content including information indicating the number of searches for each search target corresponding to the category whose selection was accepted in the reception step and the growth rate of the number of searches;
The production step includes:
an extraction processing step of extracting, as search targets, search targets whose growth rate score, which is a score obtained by quantifying the growth rate, is equal to or higher than a first threshold among the search targets corresponding to the category for which the selection has been accepted;
a generation processing step of generating information including information indicating the number of searches and the growth rate of the extraction search target as the content,
The reception process includes:
accepting the setting of the first threshold;
The extraction processing step is
Extracting the extraction search target based on the first threshold whose setting is accepted in the accepting step.
An information processing method characterized by: A reception procedure for accepting category selection;
causing a computer to execute a generation procedure for generating content including information indicating the number of searches for each search target corresponding to the category whose selection is accepted in the acceptance procedure and the growth rate of the number of searches ;
The generation procedure is
Information indicating the number of searches and the growth rate for each search target corresponding to the category for which the selection has been accepted, with the number of searches as one axis of the horizontal axis and the vertical axis and the growth rate as the other axis. Generate graph content as the content,
In the graph content, each of the search targets is shown in a manner according to an attribute score that is a score related to the attributes of the search user.
An information processing program characterized by: A reception procedure for accepting category selection;
causing a computer to execute a generation procedure for generating content including information indicating the number of searches for each search target corresponding to the category whose selection is accepted in the acceptance procedure and the growth rate of the number of searches;
The number of searches is
is the number of searches per set period, which is the set period,
The generation procedure is
The number of searches for search targets that have a growth rate score, which is a score that quantifies the growth rate, of the search targets corresponding to the category for which the selection has been accepted and that are equal to or higher than a first threshold, in a period earlier than the set period. an extraction processing procedure for extracting search targets for which the ratio of the number of searches in the set period to
a generation processing procedure for generating information including information indicating the number of searches and the growth rate of the extraction search target as the content,
The reception procedure is as follows:
accepting the setting of the second threshold;
The extraction processing procedure is
Extracting the extraction search target based on the second threshold whose setting is accepted by the acceptance procedure.
An information processing program characterized by: A reception procedure for accepting category selection;
causing a computer to execute a generation procedure for generating content including information indicating the number of searches for each search target corresponding to the category whose selection is accepted in the acceptance procedure and the growth rate of the number of searches;
The generation procedure is
an extraction processing procedure for extracting, as search targets, search targets whose growth rate score, which is a score obtained by quantifying the growth rate, is equal to or higher than a first threshold among the search targets corresponding to the category for which the selection has been accepted;
a generation processing procedure for generating information including information indicating the number of searches and the growth rate of the extraction search target as the content,
The reception procedure is as follows:
accepting the setting of the first threshold;
The extraction processing procedure is
Extracting the extraction search target based on the first threshold whose setting is accepted by the acceptance procedure.
An information processing program characterized by:

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