JP6171061B2 - Information aggregation device, information aggregation method, and information aggregation program - Google Patents

Description

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

  In recent years, popularity rankings of websites are known. The popularity ranking is aggregated based on the number of user accesses from a search site (search engine), and can be used as an index for measuring the popularity of popular sites, stores, facilities, products, and the like.

  As a technique for measuring such popularity, for example, in the invention described in Patent Document 1, in response to a keyword search request from a browser of a user terminal, link information to a Web page hit by the search is extracted, and the browser , Based on the access history (log information) to each Web page supplied from the provider's server, for example, the popularity of each Web page is obtained based on the number of accesses, etc. By sorting the link information to each Web page in descending order and sending them to the browser of the user terminal, the link information is displayed in descending order of popularity on the browser of the user terminal.

  Further, for example, in the invention described in Patent Document 2, a search is performed based on a keyword input by a user, a search is performed in the past using the same keyword as the keyword used for the current search, and the current search is performed. The number of accesses for each presentation information accessed by other users other than the user who entered the keyword used is calculated, and the order based on the calculated number of accesses is preset from the first place. The ranking information indicating the presentation information up to the ranking is generated as the search result screen information and displayed.

JP 2003-281179 A JP 2008-250662 A

  By the way, in recent years, there is an actual situation that an operator of a store or facility has a plurality of Web sites (Web pages) at the same time. For example, in the case of a restaurant, in addition to having an original Web site, many external gourmet sites (Web media) often post their own stores.

  However, in the inventions described in Patent Documents 1 and 2, since the number of accesses is totaled for each Web page, the popularity ranking can only be totaled for each single Web site even in the case of a store or facility having a plurality of Web sites. . That is, when the popularity rankings of stores and facilities having a plurality of Web sites are totaled, there is a problem that it is difficult to accurately grasp how popular the entire Web site is.

  The present invention has been proposed in view of the above points, and an object of the present invention is to widely aggregate popularity of stores and facilities across a plurality of Web sites on the Internet.

  In order to solve the above problems, an information totalization apparatus according to the present invention includes, for each of a plurality of Web sites, an acquisition unit that acquires data indicating user behavior related to the Web site, and the plurality of the Web sites. And a totaling unit that counts the degree of interest of the user with respect to a store or facility associated with the plurality of websites based on data indicating user behavior acquired for each website.

  According to the embodiment of the present invention, the popularity of shops and facilities can be widely tabulated across a plurality of Web sites on the Internet.

1 is a network configuration diagram of a ranking information aggregation system 100. FIG. It is a figure which shows the hardware structural example of the ranking information totaling server 3. FIG. 3 is a diagram illustrating a software configuration example of a ranking information totaling server 3. FIG. It is a figure which shows the example of data of user action log DB2. It is a figure which shows the example of data of name collation DB301a. It is a flowchart which shows the ranking totaling process of the ranking information totaling server 3. An example of totaling user behavior data is shown. The example of a total result of user action data is shown. It is a figure which shows the example of a ranking result of the store based on user action data. It is a figure which shows the example of an output of a ranking result.

  Hereinafter, preferred embodiments of the present invention will be described.

<Configuration>
(Network configuration)
FIG. 1 is a network configuration diagram of a ranking information totaling system 100 according to an embodiment of the present invention. In the ranking information totaling system 100, a search site server 1, a user action log DB (Database) 2, a ranking information totaling server 3, and a terminal 4 are connected via a network 5.

  The search site server 1 is configured as a so-called search engine for searching information on the Internet for the user terminal 4. For example, when a search query (search word) is input from the user terminal 4, the search site server 1 searches the Internet for information (for example, a website) that matches the search query and outputs the search result. .

  The user behavior log DB 2 is a database in which index data (hereinafter also referred to as user behavior data) indicating user behavior related to a website is accumulated. Specifically, in the user behavior log DB 2, as an example of data indicating the user behavior with respect to the website, the number of clicks on the web page, the number of browses of the website, the staying time, the unique user for each website (for each URL) Number, bounce rate, withdrawal rate, SNS share number, bookmark registration number, and the like are accumulated.

  The ranking information totaling server 3 uses the user behavior data in the user behavior log DB 2 to aggregate the user behavior data of a plurality of websites on which a single store or facility is posted, collectively. Moreover, the ranking information totaling server 3 performs popularity ranking (ranking) of stores or facilities according to the totaling result of user behavior data. This point will be described later in detail again.

  The terminal 4 is a user terminal used by the user. The user accesses the search site server 1 using the terminal 4 and searches for information on the Internet (for example, a Web site) by inputting a search query from the search screen. The search results display a list of Web sites (links) that match the search query, and the user browses the Web site by clicking on the Web site to be browsed from the list. The terminal 4 also shares the website with the SNS, registers bookmarks, and the like.

  The network 5 is a network including wired and wireless, and is, for example, an Internet network that connects the search site server 1 and the terminal 4.

(Hardware configuration)
FIG. 2 is a diagram illustrating a hardware configuration example of the ranking information totaling server 3 according to the embodiment of the present invention. As shown in FIG. 2, the ranking information totaling server 3 includes a CPU (Central Processing Unit) 31, a ROM (Read Only Memory) 32, a RAM (Random Access Memory) 33, and an HDD (Hard Disk Drive) as hardware configurations. 34, an input device 35, a display device 36, and a communication device 37.

  The CPU 31 executes various programs and performs arithmetic processing. The ROM 32 stores a program necessary for starting up. The RAM 33 is a work area for temporarily storing processing performed by the CPU 31 and storing data. The HDD 34 stores various data and programs. The input device 35 is a keyboard or a mouse, for example. The display device 36 is a display. The communication device 37 communicates with other devices.

(Software configuration)
FIG. 3 is a diagram showing a software configuration example of the ranking information totaling server 3 according to the embodiment of the present invention. The ranking information totaling server 3 includes a storage unit 301, a log acquisition unit 302, a totaling unit 303, a ranking unit 304, and an output unit 305 as main functional units.

  The storage unit 301 stores a name identification DB 301a. In the name identification DB 301a, one store or facility identifier (store ID or the like) and a plurality of Web sites (store URL or the like) related to the one store or facility are stored in advance. The name identification DB 301a is used when the user behavior data of a plurality of websites related to a store or a facility is aggregated in a cross-sectional manner.

  The log acquisition unit 302 acquires accumulated user behavior data from the user behavior log DB2. The log acquisition unit 302 can select and acquire only user behavior data corresponding to a specified condition. For example, when the target period to be acquired is designated, the log acquisition unit 302 selects and acquires only the user action data recorded in the target period from the user action log DB2. The user action log DB 2 may be installed in an external device (storage device) or may be arranged in the ranking information totaling server 3. Further, the user behavior data itself of the user behavior log DB 2 may be provided by another trader.

  The totaling unit 303 collectively summarizes user behavior data of a plurality of websites related to one store or facility using the user behavior data acquired from the user behavior log DB2 while referring to the name identification DB 301a. To do.

  As described above, the user behavior data includes the click log to the Web page, the number of browsing of the Web site, the staying time, the number of unique users, the bounce rate, the withdrawal rate, and the SNS for each Web site (for each URL). The number of shares and the number of bookmarks registered. That is, the user behavior data can be used as an index indicating the degree of user interest in the store or facility. For example, the more the click log and the number of browsing, the higher the degree of interest of the user with respect to the website. For this reason, crossing a plurality of websites on the Internet and summing up the user behavior data of a plurality of websites related to a single store or facility collectively means the user's interest in the store or facility It can be said that the data is widely counted.

  The ranking unit 304 ranks the store or facility by ranking the user behavior data for each store or facility that is aggregated by the aggregation unit 303. The user behavior data indicates the degree of user interest in the store or facility. For this reason, as the ranking or ranking of the user behavior data becomes higher, more users are more interested. That is, such stores or facilities can be referred to as so-called popular stores and popular facilities.

  The output unit 305 outputs the ranking result of the store or facility. The output is output in a form that is easy for the user to see, for example, first, second, third, etc., and can be introduced at a corner introducing a store or facility of the portal site.

  The above is the main functional configuration of the ranking information totaling server 3. Each of these functions is actually realized by causing the computer to implement the program executed by the CPU 31 of the ranking information totaling server 3.

(User action log DB2)
FIG. 4 is a diagram showing a data example of the user action log DB 2 according to the embodiment of the present invention. For each “URL” 400, the user behavior log DB 2 includes “click count” 401, “view count” 402, “stay time” 403, “unique user count” 404, “bounce rate” 405, “leave rate” 406. , “Number of shares to SNS” 407, “Number of bookmark registrations” 408, and the like.

  “URL” 400 is a URL (Uniform Resource Locator) indicating the location of the Web site.

  “Number of clicks” 401 is the number of times the user clicked on the Web site from the search result list provided by the search site server or from articles such as news and blogs. The “number of clicks” 401 can be acquired from a click log to each Web site in the search site server 1 or a click log of a Web site from articles such as news and blogs.

  “Number of browsing” 402 is the number of times the user browses the website. Also called the number of accesses.

  “Stay time” 403 is the time (= viewing time) when the user stayed on the website when the user browsed (visited) the website.

  “Number of unique users” 404 is the number of unique users who browse (visit) the website. In addition, since it means the number of unique users, even if the same user browses (visits) the website a plurality of times, it is counted as 1.

  The “bounce rate” 405 is a ratio / ratio where a user who visits the Web site sees only the first page of the entrance and leaves the Web site (exits the Web site).

  “Leaving rate” 406 is a rate / ratio where a user who has visited the website has left the website without making a final result such as product purchase, document request, restaurant seat reservation, etc. is there.

  “Number of shares to SNS” 407 is the number of times that the website has been shared with various SNSs (social networking services).

  “Number of bookmark registrations” 408 is the number of times the Web site is registered in the browser bookmarks.

  It can be said that the degree of the user's interest in the store or facility is higher as the value of the above user behavior data is larger. However, the “bounce rate” 405 and “leave rate” 406 are reciprocal numbers, and the greater the reciprocal value, the greater the degree of interest of the user in the store or facility.

  Note that any of these user behavior data can be obtained by applying various click log analysis techniques, access analysis, and the like based on various web logs for the website.

(Name identification DB 301a)
FIG. 5 is a diagram showing a data example of the name identification DB 301a according to the embodiment of the present invention. The name identification DB 301a includes data such as “store ID” 501, “store name” 502, “NG flag” 503, “store URL” 504, and the like.

  “Store ID” 501 is an ID (unique identification information) of a store or facility registered in the name identification DB 301a. It is numbered when registered in the name identification DB 301a.

  “Store name” 502 is the name of the store or facility registered in the name identification DB 301a. In addition to the “store name” 502, there may be various attribute information related to the store and facility such as “location” and “genre” (not shown).

  The “NG flag” 503 is one of attribute information and takes a value of 0 or 1. When the flag 1 is attached to the “NG flag”, it is excluded (NG) from the popularity ranking. As a result, it is possible to prevent the ranking from becoming higher due to negative reasons or topics. When there is a store to be excluded (NG) from the ranking, the manager adds a flag 1 to the “NG flag”.

  “Store URL” 504 is a URL of a Web site related to the store or facility. When the store or facility has a plurality of Web sites, the URL of all Web sites is registered in “Store URL” 504. Here, the “Store URL” 504 website includes different information such as a store introduction page in a gourmet portal site operated by different companies as well as the original website operated by the store itself. Includes websites where store information or facility information on the site is introduced and posted.

For example, a restaurant called “Japanese Restaurant AAA” has three websites (Web pages) in a gourmet portal site that collects information on restaurants, and one original website operated by the restaurant itself. It is assumed that there are four websites (web pages).
(Web site of “Japanese food AAA” in the gourmet site)
http://oishi.com/tokyo/shibuya/F12345/
http://taberu.co.jp/tokyo/S00021/
http://haizen.com/japanesefood/ID5536525/
(Original website)
http://washo-ku.co.jp/
In this case, the administrator registers the URLs of these four Web sites in advance as Web sites related to the restaurant “Japanese restaurant AAA” in association with “Japanese restaurant AAA”. The update of the “store URL” 504 can be sequentially updated by the administrator, but when a new “Japanese food AAA” website is discovered by analysis of a search robot or the like, this website is automatically updated ( It is also possible to add). In addition, when a registered website is lost, the website can be automatically updated (deleted).

Further, the “store URL” 504 includes a child site by registering only the parent site when there is a parent-child relationship in the Web site. For example, it is assumed that the top page of the “Japanese food AAA” website in a gourmet site is “http://oishi.com/tokyo/shibuya/F12345/”. Further, it is assumed that the following child pages such as “menu”, “notice”, and “service” exist from the parent page (top page), for example.
http://oishi.com/tokyo/shibuya/F12345/menu
http://oishi.com/tokyo/shibuya/F12345/infomation
http://oishi.com/tokyo/shibuya/F12345/service
In this case, the “store URL” 504 of the name identification DB 301a includes all child pages under the folder “F12345” simply by registering the parent page “http://oishi.com/tokyo/shibuya/F12345/”. can do. It is not necessary to register every single child page (registration itself is possible).

When the store has a plurality of group stores, the “store URL” 504 includes, for example, a folder of the original website (for example, http://washo-ku.co.jp/) as follows: The URL for each group store is registered for each subordinate.
http://washo-ku.co.jp/shibuya (Shibuya store)
http://washo-ku.co.jp/ginza (Ginza store)
<Operation>
FIG. 6 is a flowchart showing the ranking tabulation process of the ranking information tabulation server 3 according to the embodiment of the present invention. Hereinafter, it will be described in detail with reference to the drawings.

  As a premise, user behavior data (FIG. 4) is accumulated in the user behavior log DB2, and various data such as URLs of websites related to restaurants are registered in the name identification DB 301a. .

  S1: The ranking information totaling server 3 determines whether or not a ranking information acquisition request has been received.

Here, the ranking information acquisition request includes a condition for designating a target range of ranking aggregation. For example, there are the following conditions.
Target period: Target user such as 1 year, 6 months, 3 months, 1 month, 1 week, 1 day, 12 hours, 1 hour, 30 minutes, 10 minutes, day of the week, or specific date Attributes: Gender (male or female), age / age group, address or residence area, occupation / job type, etc. S2: Next, the log acquisition unit 302 includes user action data stored in the user action log DB2. From the above, user behavior data that satisfies the conditions specified in the ranking information acquisition request is acquired.

  For example, when the target period is specified, the log acquisition unit 302 selects and acquires user action data accumulated in the target period that matches the specified target period from the user action log DB 2.

  When the target user attribute is specified, the log acquisition unit 302 selects and acquires user action data of a user (user ID) that matches the specified target user attribute from the user action log DB 2. For example, when “gender (male)” is designated as the target user attribute, the log acquisition unit 302 specifies “gender” for each “user ID” using a user DB (not shown) or the like. Thus, the user behavior data of “user ID” whose user “sex” is male is selected and acquired.

  S3: Next, the totalization unit 303 uses the user behavior data acquired in S2 to refer to the name identification DB 301a and, for each “store ID” 501, user behavior data of a plurality of websites related to the store. Sum up across the board. That is, the totalization unit 303 refers to the name identification DB 301a, and when the “URL” 400 of the user behavior data acquired in S2 matches any one of the “store URL” 504, the website “URL” The user behavior data associated with 400 is tabulated together.

  The user behavior data includes “clicks” 401, “views” 402, “stay time” 403, “unique users” 404, “bounce rate” 405, “leave rate” 406, “share to SNS” There are types such as “number” 407 and “number of bookmark registrations” 408. Therefore, the data is aggregated together with the same type of chest.

  For example, “URL” 400 in the user action log DB 2 in FIG. 4 is referred to. Among these, the website “http://oishi.com/tokyo/shibuya/F12345/” (the URL on the first line in FIG. 4) is registered in “Store URL” 504 with “Store ID” 501 of ID00001. Matches one of the websites. As a result, the website “http://oishi.com/tokyo/shibuya/F12345/” is one of the websites owned by the store whose “store ID” 501 is ID00001 (= “Japanese food AAA”). It turns out that it is.

  Similarly, the website “http://taberu.co.jp/tokyo/S00021/” (URL on the second line in FIG. 4), “http://haizen.com/japanesefood/ID5536525/” (FIG. 4). 3) and “http://washo-ku.co.jp/” (the URL on the 4th line in FIG. 4), the store whose “shop ID” 501 is ID00001 has. It turns out that it is a website.

  The totaling unit 303 determines that these four websites are the websites of the store whose “store ID” 501 is ID00001, and aggregates the user behavior data of these four websites in a cross-sectional manner (summing up) )

  FIG. 7 shows an example of aggregation of user behavior data according to an embodiment of the present invention. As shown in FIG. 7, user behavior data obtained from each of the four Web sites owned by the store with ID00001 (= “Japanese food AAA”) is aggregated for each same type. I understand that. The tabulated user behavior data is total user behavior data for all websites of the store with ID00001 (= “Japanese food AAA”).

Note that when the user behavior data of the website is aggregated, weighting may be performed according to the website. Among the gourmet portal sites, there are sites that have many pages that place emphasis on the contents of dishes and services, and sites that have many pages that place emphasis on preferential service information such as store coupons. In this case, for example, the user behavior data of the gourmet site “http://oishi.com/”, which has many pages that place importance on the contents of the dish, is multiplied by a coefficient × 1.2 which is a weighting value. As a result, the user behavior data of websites with many pages that emphasize the contents of dishes is 1.2 times larger than the actual ones, compared to websites with many pages that emphasize the publication of preferential service information. The value of user behavior data as an evaluation can be adjusted. On the other hand, for example, the user behavior data on a page that places importance on the placement of preferential service information is multiplied by a factor of 0.5. Thereby, if it is a restaurant store, the user behavior data of the website caused by the preferential service can be reduced by half compared to the actual content.
The weighting value is determined according to the influence on the popularity and appeal of the website, and for example, each weighting value can be stored for each “store URL” 504 in the name identification DB 301a. .

  FIG. 8 shows an example of a totaling result of user behavior data according to an embodiment of the present invention. As shown in FIG. 8, each store “ID” 501 (= “Japanese food AAA”), ID00002 (= “Kaiseki BBB”) ID00003 (= “Noodles and XXX”) Each time, the user behavior data of a plurality of related Web sites are aggregated in a cross-sectional manner.

  Moreover, you may weight according to the attribute information of a store with respect to the user action data of the store totaled cross-sectionally. For example, the user behavior data of “Japanese food AAA” whose store attribute information “genre” is “Japanese food” is multiplied by a coefficient × 1.2 which is a weighting value. Thereby, the user behavior data of the store can be increased by 1.2 times than the actual value, and the value of the user behavior data can be adjusted according to the “genre” of the store. On the other hand, for example, the user behavior data of a store whose store attribute information is “genre” other than “Japanese food” is multiplied by a coefficient × 0.5. The weighting value is determined according to the store, and for example, each weighting value can be stored for each “store ID” 501 of the name identification DB 301a.

  S4: Next, the ranking unit 304 ranks the stores by ranking the stores based on the user behavior data for each “store ID” 501 aggregated by the aggregation unit 303. The user behavior data indicates the degree of user interest in the store or facility. For this reason, as the number of stores or facilities with higher rankings becomes higher, more users are more interested. That is, such stores or facilities can be referred to as so-called popular stores and popular facilities.

  FIG. 9 is a diagram illustrating an example of a ranking result of stores based on user behavior data according to an embodiment of the present invention. In the case of FIG. 9, “Store ID”: a store with ID00001 (kaiseki cuisine BBB) is ranked first.

  Here, the user behavior data includes “clicks” 401, “views” 402, “stay time” 403, “unique users” 404, “bounce rate” 405, “leave rate” 406, “to SNS” There are “number of shares” 407, “number of bookmark registrations” 408, and the like. For this reason, it is possible to output popularity ranking using any data among these user action data.

  For example, it is assumed that the popularity ranking is output using “clicks” 401 in the user behavior data. In this case, a restaurant called “Kaishi Cuisine BBB” has three gourmet sites (FIG. 5), and the total number of “clicks” to these three gourmet sites is even compared to other stores. The biggest. This means that the website of “Kaiseki BBB” in the three gourmet sites was clicked by the most users. That is, “Kaishi Cuisine BBB” is the most popular and popular store because the website related to “Kaiseki Cuisine BBB” is widely attracted by most users compared to other stores.

  It should be noted that it is possible to output the popularity ranking using not only one of the user behavior data (for example, not only “number of clicks” 401) but also two or more data or all data.

  In addition to these user behavior data, for example, parameters (evaluation values) for measuring the popularity of the store, such as the number of reviews, word-of-mouth evaluation (number of stars, points, etc.), topicality, and other scores Including the ranking for each store is also possible. In this case, since ranking is performed based on various parameters that can measure the popularity of the store as well as user behavior data related to the website for each store, the popularity of the store can be measured from a multifaceted perspective. It becomes possible. It is also possible to rank each store as a recommended order including parameters indicating the convenience of the store, such as the distance from the store station and whether reservation is possible.

  Further, as described above, when the flag is added to the “NG flag” which is one of the store attribute information, the ranking unit 304 excludes (NG) from the ranking. As a result, it is possible to prevent the number of clicks from increasing due to a negative reason or topic, and thus becoming a higher ranking.

  S5: The output unit 305 outputs the ranking result of the store. The output is output in a form that is easy for the user to see, for example, first, second, third, etc., and can be introduced at a corner introducing a store or facility of the portal site. It may be provided to TV stations as information on popular stores and popular facilities, and may be taken up in TV programs.

  FIG. 10 is a diagram illustrating an output example of ranking results according to the embodiment of the present invention. In the case of FIG. 10, “store ID”: store ID001 (kaiseki cuisine BBB) is ranked first in the ranking based on the user behavior data. As shown in FIG. 8, when keywords such as “Shibuya-ku” and genre “Japanese food” are input, the input keyword is compared with the attribute information of the store (for example, “location” and “genre”). By doing so, the ranking of the store may be output from the range of the target store, targeting the store corresponding to the input keyword. Therefore, for example, “noodles and XXX” in FIG. 5 are not included in the ranking because they are not stores corresponding to keywords such as “Shibuya-ku” and genre “Japanese food”.

  In the case of a store having a plurality of group stores, if only the top page URL (for example, http://washo-ku.co.jp/) is registered in the “store URL” 504, all group stores Therefore, it is not possible to divide and compile for each subordinate store. Therefore, first, the user behavior data corresponding to the top page URL of the store group is equally allocated to each store. Further, the user action data to be assigned is adjusted based on the ratio of the “number of clicks” 401 thereafter. For example, if the top page URL includes “clicks” 401 of 100 times, at the start, the Shibuya store is assigned 50 times and the Ginza store is assigned 50 times. If the ratio of “clicks” 401 thereafter is Shibuya store: Ginza store = 1: 3, the Shibuya store may be 25 times and the Ginza store may be 75 times according to the ratio.

<Supplement>
It supplements about the parameter | index based on user action data.

  In S2, by acquiring user behavior data that satisfies the conditions specified in the ranking information acquisition request from the user behavior data stored in the user behavior log DB2, in S3, the user behavior acquired in S2 Using the data as a target range, the user behavior data of the website is aggregated for each “store ID”.

  Therefore, by adding conditions such as the target period and user attributes in the ranking information acquisition request, it is possible to specify conditions for the target range of user behavior data that is subject to ranking aggregation. That is, the user behavior data satisfying the conditions can be tabulated across a plurality of Web sites, and the stores can be ranked.

  As a specific example, for example, it is possible to rank stores under the following index.

(Example 1)
The user behavior data of the website is tabulated for each “store ID” for user behavior data whose target period satisfies the conditions such as holidays (or weekends), this week, and this month. Accordingly, it is possible to rank stores while limiting the target period based on indexes such as “order of popularity on holidays”, “order of popularity this week”, and “order of popularity this month”.

  For this reason, for example, the store ranking “in order of popularity of holidays” allows the user to easily find a popular store that he / she wants to visit on a weekend holiday. In addition, for example, the store ranking “in order of popularity of this week” allows the user to easily find the latest popular store of interest.

  In addition, it is possible to output as an index such as “in order of rapid increase in popularity” by ranking the stores where the user behavior data increase during the target period is remarkable.

(Example 2)
For user behavior data whose user attribute (gender) satisfies conditions such as male and female, Web site user behavior data is aggregated for each “store ID”. Accordingly, it is possible to rank popular stores among users whose user attributes (gender) satisfy the condition by using an index such as “in popularity with men” or “in popularity with women”.

  For this reason, for example, the store ranking “Popularity for women” makes it possible for the user to easily find a store popular with women when a women's association is held.

(Example 3)
The user behavior data of the website is tabulated for each “store ID” for user behavior data whose user attributes (age / age group) satisfy conditions such as teenagers and twenties. Accordingly, it is possible to rank popular stores among users whose user attributes (age / age group) meet the conditions based on indexes such as “in order of popularity of teens” and “in order of popularity of 20s”.

  For this reason, for example, the user can easily find a popular store for each age group based on the store ranking of “in order of popularity of teenagers”.

(Example 4)
For user behavior data whose user attribute (address or residence area) satisfies conditions such as Tokyo, Osaka, etc., the user behavior data of the website is aggregated for each “store ID”. Accordingly, it is possible to rank popular stores among users whose user attributes (address or residence area) meet the conditions based on indices such as “Popularity in Tokyo” and “Popularity in Osaka”.

  For this reason, for example, according to the store rankings “in order of popularity in Tokyo” and “in order of popularity in Osaka”, the user can easily find popular stores among local people.

(Example 5)
The user behavior data of the website is aggregated for each “store ID” for user behavior data whose user attributes (occupation / job type) satisfy the conditions such as company employee and self-employed. This makes it possible to rank popular stores among users whose user attributes (profession / job type) meet the conditions based on indices such as “in order of popularity of company employees” and “in order of popularity of civil servants”.

  For this reason, for example, by referring to the store ranking “in order of popularity of company employees”, the user can easily find a popular store among the employees who return to the company.

<Summary>
As described above, according to the embodiment of the present invention, it is possible to widely aggregate the popularity of stores and facilities across a plurality of Web sites on the Internet.

  The present invention has been described with reference to preferred embodiments of the present invention. While the invention has been described with reference to specific embodiments, various modifications and changes may be made to these embodiments without departing from the broad spirit and scope of the invention as defined in the claims. Obviously you can. In other words, the present invention should not be construed as being limited by the details of the specific examples and the accompanying drawings.

1 Search site server 2 User action log DB
3 Ranking information totaling server 4 Terminal 5 Network 31 CPU
32 ROM
33 RAM
34 HDD
35 Input Device 36 Display Device 37 Communication Device 100 Ranking Information Total System 301 Storage Unit 301a Name Identification DB
302 Log acquisition unit 303 Totaling unit 304 Ranking unit 305 Output unit

Claims (10)

Obtaining means for obtaining data indicating user behavior related to the website for each of the plurality of websites;
Based on data indicating user behavior acquired for each of the plurality of websites, a counting unit that counts the degree of interest of the user with respect to a store or facility associated with the plurality of websites;
An information totalizing apparatus comprising: Data indicating user behavior on the website is:
Click log to the Web page accessed from the list of query search results, number of browsing of the Web site, staying time, number of unique users, bounce rate, withdrawal rate, number of shares to SNS, or number of bookmark registration Including at least one of
The information totaling device according to claim 1, wherein: Ranking means for ranking the store or facility according to a parameter including the degree of interest of the user with respect to the store or facility;
The information totaling apparatus according to claim 1, wherein the information totaling apparatus comprises: The ranking means includes
If the store or facility attribute information has a predetermined flag, excluding the store or facility having the predetermined flag from the ranking target,
The information totalization device according to claim 3 characterized by things. A plurality of websites related to the store or facility are:
Each is a URL of a website containing store information or facility information on different information sites,
The information totaling device according to any one of claims 1 to 4, wherein The acquisition means includes
When a condition including at least one of a period in which data indicating user behavior is recorded or a user attribute of a user who has performed user behavior is specified, obtaining data indicating the user behavior satisfying the condition,
The information totaling device according to any one of claims 1 to 5, wherein The counting means includes
The weighted data is weighted by multiplying the data indicating the user behavior acquired for each of the plurality of websites by a coefficient determined for each store or facility, and weighting the data indicating the user behavior. Aggregating the degree of interest of the user with respect to a store or facility associated with the plurality of websites based on data indicating behavior;
The information totaling device according to any one of claims 1 to 6. The counting means includes
After the user's interest level for the store or facility is aggregated, the user's interest level is weighted by multiplying the user's interest level by a coefficient determined for each store or facility,
The information totaling device according to claim 1, wherein An acquisition procedure in which the information aggregation device acquires data indicating user behavior related to the website for each of the plurality of websites;
Based on data indicating user behavior acquired for each of the plurality of websites, a counting procedure for totalizing a user's degree of interest in a store or facility associated with the plurality of websites;
Information summarizing method characterized by executing. On the computer,
Obtaining means for obtaining data indicating user behavior related to the website for each of the plurality of websites;
An information counting program that functions as a counting unit that counts the degree of interest of a user for a store or facility associated with a plurality of websites based on data indicating user behavior acquired for each of the plurality of websites.

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