JP2014228969A - Content retrieval result providing device and content retrieval result providing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a content retrieval result providing device and content retrieval result providing method capable of enabling a user to easily confirm various kinds of content as a retrieval result.SOLUTION: An information distribution server 200 comprises: a related content acquisition unit 205 and a query retrieval result acquisition unit 206 for acquiring a plurality of content retrieval results against retrieval queries; a content summarization unit 208 for summarizing the content retrieval results acquired by the related content acquisition unit 205 and the query retrieval result acquisition unit 206 for each prescribed similar range; and a content retrieval result output unit 209 for outputting the content retrieval results summarized by the content summarization unit 208.

Description

  The present invention relates to a content search result providing apparatus and a content search result providing method.

  2. Description of the Related Art Conventionally, a technique for providing content that a user thinks is desired based on information on content that the user has used in the past is known. For example, in the technique described in Patent Document 1, the feature level for each item is calculated for each user based on the meta information in the user history information, and the content to be recommended is acquired from the value of the feature level for each item. ing.

JP 2004-362011 A

  By the way, when content is provided based on content information used in the past using the above-described technology, many similar contents are easily provided as search results. For this reason, many similar contents are displayed on the terminal displaying the search result, and it is difficult for the user to check various types of contents as the search result. Such a problem becomes particularly serious when the terminal displaying the search result is a small terminal such as a mobile phone.

  The present invention has been made in view of the above circumstances, and an object thereof is to provide a content search result providing apparatus and a content search result providing method by which a user can easily confirm various types of content as search results.

  A content search result providing apparatus according to the present invention includes a search result acquisition unit that acquires a plurality of content search results for a search query, and a content that aggregates the content search results acquired by the search result acquisition unit for each predetermined similarity range An aggregation unit; and an output unit that outputs a content search result aggregated by the content aggregation unit.

  The content search result providing method according to the present invention includes a search result acquisition step for acquiring a plurality of content search results for a search query, and aggregating the content search results acquired in the search result acquisition step for each predetermined similarity range. A content aggregation step, and an output step of outputting the content search results aggregated in the content aggregation step.

  According to such a content search result providing apparatus and content search result providing method, content search results for a search query are aggregated and output for each predetermined similar range. As a result, the content search results after being aggregated for each predetermined similar range are displayed on the user terminal, and the number of similar content displayed is reduced. Since the number of similar contents to be displayed is reduced, various types of content are easily displayed as compared with the conventional case, and the user can easily check various types of content as a search result.

  In the content search result providing apparatus according to the present invention, the content aggregation means may aggregate the content search results with a genre indicating the type of each content as a predetermined similarity range. According to the present invention, content search results are aggregated by genre indicating the type of content, and content having similar characteristics can be appropriately aggregated.

  In the content search result providing apparatus according to the present invention, the content aggregation means may determine the number of search results according to the importance for each predetermined similar range, and aggregate the content search results. According to the present invention, it is possible to display various types of contents while displaying a large number of search results of important similar ranges.

  The content search result providing apparatus according to the present invention further includes a similar range structure storage unit that stores a similar range structure in which a plurality of predetermined similar ranges are hierarchically structured, and the content aggregation unit is included in the similar range structure storage unit. Based on the stored similar range structure, the content search results of each similar range may be aggregated. According to the present invention, content search results can be easily aggregated based on a hierarchical structure of similar ranges.

  In the content search result providing apparatus according to the present invention, the content aggregating unit determines a reference similar range as a reference among the similar ranges, and uses an identifier for specifying the reference similar range and an identifier for specifying each similar range. Based on this, content search results for each similar range may be aggregated. By determining the reference similarity range and aggregating the content search results of each similar range based on the reference similarity range, the content search results of each similar range can be more easily aggregated.

  The content search result providing apparatus according to the present invention further includes content evaluation means for calculating an evaluation value of each content included in the content search result acquired by the search result acquisition means, and the content aggregation means includes the content evaluation means. The reference similarity range may be determined based on the evaluation value of the content search result calculated by. By setting a genre having a high evaluation value as the reference similarity range, the similarity range related to the content desired by the user can be set as the reference similarity range.

  According to the present invention, it is possible for a user to easily check various types of content as search results.

It is an information system block diagram of this embodiment. It is a block diagram which shows the function of the information delivery server 200 of this embodiment. 2 is a hardware configuration diagram of an information distribution server 200. FIG. It is a figure which shows the data structure of content historical information. It is a figure which shows the data structure of the information which the content metadata storage part 202 manages. 10 is a table showing content search results for which an evaluation value is calculated by a content evaluation unit 207. It is the figure which represented typically the genre structure which the genre structure memory | storage part 210 memorize | stores. It is a table | surface which shows the genre structure table which the genre structure memory | storage part 210 memorize | stores. It is a table | surface which shows the association degree table which the genre structure memory | storage part 210 memorize | stores. It is a table | surface which shows the content search result aggregated for every genre. It is a block diagram which shows the function of the recommendation server 300 of this embodiment. It is a block diagram which shows the function of the search server 400 of this embodiment. It is a block diagram which shows the function of the user terminal 100 of this embodiment. It is a sequence diagram of this embodiment. It is a flowchart of the detailed process of content evaluation calculation. It is a flowchart of the detailed process of content aggregation.

  Embodiments according to the present invention will be described below with reference to the drawings. Where possible, the same parts are denoted by the same reference numerals, and redundant description is omitted.

(Configuration of information processing system)
As illustrated in FIG. 1, the information processing system includes an information distribution server 200 (content search result providing device), a recommendation server 300, and a search server 400. A user terminal 100 such as a mobile phone is connected to the information distribution server 200 via a network.

  When the information distribution server 200 receives the search query from the user terminal 100 and accepts the search request, the information distribution server 200 uses the information on the content selected from the search result based on the past search query and relates to the search query. The content having the characteristic is determined, and the content related to the content and the content corresponding to the search query are provided to the user terminal 100. Note that the selection here means selection for a predetermined selection means (link, button, etc.), not only selection that means browsing (for example, link selection of content detailed information, etc.) but also the purpose of purchase. (For example, link selection for content purchase). Here, the content includes not only the content content but also content identification information.

  FIG. 2 is a block diagram showing a functional configuration of the information distribution server 200 of the present embodiment. As shown in FIG. 2, the information distribution server 200 includes a content history information storage unit 201, a content metadata storage unit 202, a search request reception unit 203, a recommendation acquisition target content determination unit 204, a related content acquisition unit 205 (search results) Acquisition unit), query search result acquisition unit 206 (search result acquisition unit), content evaluation unit 207 (content evaluation unit), content aggregation unit 208 (content aggregation unit), content search result output unit 209 (output unit), and genre The structure storage unit 210 (similar range structure storage means) is included.

  FIG. 3 is a hardware configuration diagram of the information distribution server 200. As shown in FIG. 3, the information distribution server 200 shown in FIG. 1 physically includes one or more CPUs 11, a RAM 12 and a ROM 13 as main storage devices, and an input device 14 such as a keyboard and a mouse as input devices. The computer system includes an output device 15 such as a display, a communication module 16 that is a data transmission / reception device such as a network card, an auxiliary storage device 17 such as a semiconductor memory, and the like. Each function in FIG. 2 has one or a plurality of predetermined computer software loaded on the hardware such as the CPU 11 and the RAM 12 shown in FIG. 3 to control the input device 14, the output device 15, and the communication under the control of the CPU 11. This is realized by operating the module 16 and reading and writing data in the RAM 12 and the auxiliary storage device 17. Hereinafter, each functional block will be described based on the functional blocks shown in FIG.

  The content history information storage unit 201 is a database that stores content history information that is information in which past search queries are associated with content selected from search results based on the past search queries.

  FIG. 4 is an explanatory diagram showing the contents stored in the content history information. As shown in FIG. 4, the content history information includes a user ID, a search query, a selected content ID, and a date and time at the time of selection.

  In FIG. 4, for example, the user terminal 100 with the user ID “User1” makes a search request with the search query “drink”, and juice A (orange) (500 ml × 24 bottles) is obtained from the search result as of January 10, 2013. Indicates that the day has been selected.

  The content metadata storage unit 202 is a database that stores information (content metadata) that associates content with metadata of the content.

  FIG. 5 is an explanatory diagram showing the contents stored in the content metadata. As shown in FIG. 5, the content metadata includes a content ID and metadata. Here, the metadata means data search information (for example, content attribute information).

  In FIG. 5, for example, the metadata of the content ID “juice A (orange) (with 500 ml × 24 bottles)” is “product design that is nice for soft drinks / soft drinks, drinks / soft drinks, carbonic acid, and body”. Show.

  The search request receiving unit 203 acquires a user ID and a search query from the user terminal 100 and receives a content search request.

  The recommendation acquisition target content determination unit 204 determines the recommendation acquisition target content that is the content to be recommended acquisition based on the search query at the time of the search request and the content history information stored in the content history information storage unit 201. To do.

  Specifically, the recommendation acquisition target content determination unit 204 refers to the content history information and determines the content corresponding to the search query at the time of the search request as the recommendation acquisition target content. Further, the recommendation acquisition target content determination unit 204, when there is no past search query corresponding to the search query at the time of the search request (that is, when there is no content corresponding to the search query at the time of the search request), Among the contents, a recommendation acquisition target content is determined based on a search query and metadata at the time of a search request. Then, if there is no metadata based on the search query at the time of the search request among the contents of the content history information, the recommendation acquisition target content determination unit 204 determines the content of the latest content history information as the recommendation acquisition target content. The past search query corresponding to the search query at the time of the search request includes not only a past search query that completely matches the search query at the time of the search request but also a past search query that partially matches.

  The related content acquisition unit 205 notifies the recommendation server 300 of the recommendation acquisition target content determined by the recommendation acquisition target content determination unit 204, makes a related content acquisition request, and obtains the related content from the recommendation server 300 as a first search result. Get as. Here, the related content refers to content similar to the recommendation acquisition target content (for example, content having common metadata).

  The query search result acquisition unit 206 notifies the search server 400 of the search query and acquires the content corresponding to the search query from the search server 400 as the second search result.

  The content evaluation unit 207 calculates the evaluation value of the related content acquired by the related content acquisition unit 205 and the content acquired by the query search result acquisition unit 206. FIG. 6 is a table showing content search results for which evaluation values are calculated by the content evaluation unit 207. In FIG. 6, the contents hit when a search request is made with the search query “drink” are arranged in the order of evaluation values. Here, the genre of each content indicates the type of each content (category to which each content belongs). For example, the content “mountain delicious water (500 ml × 24 bottles)” represents the genre “drink / water / The content belongs to “Delicious Water of the Mountain”. The genre “drink / water / mountain delicious water” is the content related to “water” (middle concept) in “drink” (superior concept) and “mountain delicious water” (subordinate concept) It is shown that.

  The content aggregation unit 208 aggregates the related content acquired by the related content acquisition unit 205 and the content acquired by the query search result acquisition unit 206 for each predetermined similar range. Here, the predetermined similarity range is, for example, a genre indicating the type of each content. Note that the predetermined similar range is not limited to a genre, and may be based on other metadata or other information that can aggregate each content in a predetermined group. Below, a genre is demonstrated as a predetermined similar range.

  The content aggregating unit 208 determines the number of search results according to the importance for each genre, and aggregates the content search results. The number of search results is the upper limit number of content search results output by the content search result output unit 209. When the content aggregation unit 208 determines the number of search results for a certain genre, the content aggregation unit 208 aggregates the content search results for the genre so that the number of search results is the same. For example, even if there are 10 content search results for a certain genre, if the number of search results is determined to be 3, the content aggregating unit 208 searches the content so that the content search result for the genre is 3. Aggregate results. Note that the content aggregating unit 208 preferentially selects content having a high evaluation value calculated by the above-described content evaluation unit 207 as content to be a content search result after aggregation.

  The content aggregating unit 208 aggregates content search results for each genre based on the genre structure stored in the genre structure storage unit 210. The genre structure is a hierarchical structure of each genre, and is represented by, for example, a hierarchy shown in FIG. FIG. 7 is a diagram schematically showing the genre structure stored in the genre structure storage unit 210.

  In the genre structure, the relationship of each genre is expressed in a hierarchical structure. In the example shown in FIG. 7, “drink” and “beauty” are included as the highest genres, and “drink” is a subordinate concept “ It has “water” and “soft drink”, and “mountain delicious water” and “forest delicious water” as subordinate concepts of “water”, respectively. The contents related to “mountain delicious water” have “500 ml × 24 pieces”, “500 ml × 48 pieces × 2 cosets”, “500 ml × 48 pieces”, “3.78 l × 6 pieces”. .

  The content aggregating unit 208 determines a reference genre serving as a reference among the genres, specifies the degree of association between the reference genre and each genre from the genre structure, and determines the number of search results for each genre based on the degree of association. To do. Here, the degree of association with the reference genre is the importance of each genre described above. The content aggregation unit 208 determines a reference genre based on the evaluation value of the content search result calculated by the content evaluation unit 207. Specifically, the content aggregation unit 208 sets the genre related to the content having the highest evaluation value of the content search result as the reference genre. Since the reference genre is the genre related to the content having the highest evaluation value, it is usually the genre having the largest number of search results.

  The content aggregating unit 208 uses the genre identifier set for each genre when specifying the degree of association of each genre with the reference genre. The specification of the degree of association using the genre identifier will be described later.

  Hereinafter, an example of the determination process of the number of search results for each genre and the aggregation process of content search results by the above-described content aggregation unit 208 will be described with reference to FIGS. First, it is assumed that the content evaluation unit 207 calculates an evaluation value for each content hit when a search request is made with the search query “drink”, and the content is arranged in the order of the evaluation values shown in FIG. In this case, the content aggregating unit 208 sets “drink / water / delicious water of the mountain”, which is a genre related to “mountain delicious water (500 ml × 24 bottles)”, which is the content having the highest evaluation value, as the reference genre.

  The content aggregating unit 208 specifies the degree of association between the reference genre and each genre from the genre structure. In the following, the lowest genre in the genre structure shown in FIG. 7 (for example, “Delicious Water of the Mountain” or “My Momo!”) Is set as the lower genre, and the genre of the upper layer of the lower genre (for example, “Water” In the following description, “soft drink” and the like are defined as a middle genre, and a genre that is one level higher than the middle genre (for example, “drink” and “beauty”) is defined as an upper genre.

  The degree of association between the reference genre and each genre is specified from the degree of coincidence of the lower genre, middle genre, and upper genre. In the following order, the genre is identified as having a high degree of association with the reference genre. That is, the genre in which the lower genre is different, but the middle genre is the same as the reference genre, the genre is different, but the upper genre is the same as the reference genre, and the upper genre is different from the reference genre. Is said to be a high genre. Since the standard genre is “Drink / Water / Delicious Water of the Mountain”, the standard genre and the middle genre “Water” have the highest relevance of “Drink / Water / Delicious Water of the Forest” The relevance of “Drink / Soft Drink / My Peach!” And “Drink / Soft Drink / Delicious Orange Juice”, which are different in the middle genre but match the upper genre “Drink”, is high. The least relevant are “Beauty / Shampoo / Beautiful Hair Shampoo” and “Beauty / Treatment / Moisturizing Conditioning”, which have different upper genres.

The degree of relevance of each genre based on the genre structure is determined by a product of attenuation constants (attenuation constant g to the upper hierarchy and an attenuation constant h in the same hierarchy) for each hierarchy based on the reference genre. In the example shown in FIG. 7, the decay constant in the same hierarchy between lower genres is h ₁ , the decay constant in the same hierarchy between middle genres is h ₂ , and the decay constant in the same hierarchy between upper genres is h ₃ . An attenuation constant to the upper hierarchy between the lower genre and the middle genre is g ₁ , and an attenuation constant to the upper hierarchy between the middle genre and the upper genre is g ₂ .

For example, “drink / water / delicious water in the forest” matches the standard genre “drink / water / delicious water in the mountain” with the middle genre, so the attenuation constant h ₁ in the same hierarchy between the lower genres is related. Degree. In addition, “drink / soft drink / my peach!” Is different from the standard genre “drink / water / delicious water of the mountain” and the upper genre is different, so the upper genre is higher among the lower genre and middle genre. The product of the attenuation constant g ₁ to the hierarchy and the attenuation constant h ₂ in the same hierarchy between the middle genres is defined as the relevance. In addition, “drink / soft drink / delicious orange juice” differs from the standard genre “drink / water / mountain delicious water” because the upper genre is different and the upper genre is the same. The product of the attenuation constant g _{1 to} the value and the attenuation constant h ₂ in the same hierarchy between the middle genres is defined as the relevance. In addition, “beauty / shampoo / hair shampoo” has a different upper genre from the standard genre “drink / water / mountain delicious water”, and therefore, an attenuation constant g ₁ to the upper hierarchy between the lower genre and the middle genre The product of the attenuation constant g ₂ to the upper hierarchy between the middle genre and the upper genre and the attenuation constant h ₃ in the same hierarchy between the upper genres is defined as the relevance.

The determination of the number of search results for each genre based on the degree of association by the content aggregation unit 208 will be described. In the genre structure storage unit 210, a reference result number N that is the number of search results of a reference genre “drink / water / mountain water” preset by the user is set. Then, the content aggregating unit 208 multiplies the reference result number N by the product of the attenuation constants (g and h) that are the relevance, and determines the number of search results for each genre. Here, the reference result number N = 3, the attenuation constant g ₁ = 0.8 and g ₂ = 0.6 to the upper layer, the attenuation constant h ₁ = 0.8, h ₂ = 0.6 in the same layer, Let h ₃ = 0.4. In addition, the value calculated by multiplication is rounded off. If the calculated value is 1 or less, it is rounded up.

As described above, the lower layer genre and middle value of the attenuation constant g ₁ to the upper layer among genres (0.8) than middle genre and the value of attenuation constant g ₂ to the upper layer between the upper layer genre ( By reducing 0.6), the relevance of a genre whose upper genre does not match the reference genre can be made smaller than that of a genre whose upper genre matches the reference genre. Moreover, than the value of the damping constant h ₁ in the same layer (0.8) between the lower layer genre, reduce the value of the damping constant h ₂ in the same layer (0.6) between the middle genre, the attenuation constant than h ₂ values (0.6), by a smaller value of the attenuation constant h ₃ (0.4) in the same layer between the upper layer genre, as compared with the genre middle genre matches the reference genre The relevance of a genre in which the middle genre does not match the reference genre can be made smaller, and the relevance of the genre in which the upper genre does not match the reference genre can be made smaller than the genre in which the upper genre matches the reference genre.

In the example shown in FIG. 7, the attenuation constant in the same hierarchy between the lower genres is common to h ₁ regardless of the upper tier genre, but for each type of the upper tier genre. It may be different.

For example, the genre “drink / water / delicious water in the forest” has a relevance of h ₁ , so the number of search results is calculated as N × h ₁ = 3 × 0.8 = 2.4≈2. In addition, since the genre “drink / soft drink / my peach!” And “drink / soft drink / delicious orange juice” have a relevance of g ₁ × h ₂ , the number of search results is N × g ₁ × h ₂ = It is calculated that 3 × 0.8 × 0.6 = 1.44≈1. Further, since the genre “beauty / shampoo / hair shampoo” has a relevance of g ₁ × g ₂ × h ₃ , the number of search results is N × g ₁ × g ₂ × h ₃ = 3 × 0.8 × 0. It is calculated that .6 × 0.4 = 0.576≈1. In this way, the content aggregating unit 208 determines the number of search results so that the genre that has a high degree of association with the reference genre has a large number of search results and the genre that has a low degree of association with the reference genre reduces the number of search results. To do.

  Note that the genre structure shown in FIG. 7 is a diagram schematically illustrating the hierarchical structure, and actually, the genre structure storage unit 210 stores the genre structure table shown in FIG. In the genre names shown in FIG. 8, the left column corresponds to the upper genre, the middle column corresponds to the middle genre, and the right column corresponds to the lower genre. In the genre structure table, a genre identifier that uniquely determines a genre is set in advance. For example, the genre identifier a is set for the genre “drink / water / mountain delicious water”.

  The genre structure storage unit 210 stores the relevance for calculating the number of search results described above in the relevance table for each genre combination (for each genre identifier combination). FIG. 9 is an association degree table stored in the genre structure storage unit 210. As shown in FIG. 9, in the relevance level table, the relevance level (product of attenuation constants) in the combination of the identifier of the reference genre and the identifier of each genre is set. For this reason, the content aggregating unit 208 identifies the reference genre and the genre identifier of each genre based on the genre structure table shown in FIG. 8, and determines the reference genre and each genre based on the relevance table shown in FIG. Identify relevance. The calculation of the number of search results for each genre after specifying the relevance is as described above.

  Content search result aggregation processing by the content aggregation unit 208 is performed based on the number of search results determined for each genre. That is, as described above, the number of search results of the content search result P after the aggregation of the genre “drink / water / delicious water of the mountain” which is the reference genre is 3, and the genre “drink / water / delicious water of the forest” The number of search results of the aggregated content search result Q is 2, the number of search results of the content search result R after the genre “drink / soft drink / my peach!” And “drink / soft drink / delicious orange juice” The number of search results of the post-aggregation content search result S is 1. For this reason, the content aggregating unit 208 aggregates the content search results of each genre so that the number of search results is the same (see FIG. 10). Note that the content aggregating unit 208 preferentially selects content with a high evaluation value calculated by the content evaluation unit 207 described above as a content retrieval result after aggregation.

  The content search result output unit 209 outputs the content search result aggregated by the content aggregation unit 208 to the user terminal 100.

(Recommend server)
Next, the recommendation server 300 will be described. FIG. 11 is a block diagram illustrating a functional configuration of the recommendation server 300. As illustrated in FIG. 11, the recommendation server 300 includes a recommendation acquisition target content acquisition unit 301, a related content calculation unit 302, and a related content transmission unit 303. The recommendation server 300 is configured by hardware such as a CPU, a RAM, and a ROM, and the CPU is configured to operate in accordance with a stored program. Specifically, it has the same configuration as that of the information distribution server 200 and is realized by the hardware configuration shown in FIG. Each configuration will be described below.

  The recommendation acquisition target content acquisition unit 301 acquires the recommendation acquisition target content from the information distribution server 200.

  The related content calculation unit 302 searches the content related to the recommendation acquisition target content acquired by the recommendation acquisition target content acquisition unit 301 from a content database (not shown), and determines the related content by calculation. For example, the related content calculation unit 302 sets a content having the same metadata as that of the recommendation acquisition target content as the related content.

  The related content transmission unit 303 transmits the related content determined by the result calculated by the related content calculation unit 302 and rank information of each related content to the information distribution server 200.

(Search server)
Next, the search server 400 will be described. FIG. 12 is a block diagram illustrating a functional configuration of the search server 400. As shown in FIG. 12, the search server 400 includes a query acquisition unit 401, a content calculation unit 402, and a query search result content transmission unit 403. The search server 400 is configured by hardware such as a CPU, RAM, and ROM, and the CPU is configured to operate according to a stored program. Specifically, it has the same configuration as that of the information distribution server 200 and is realized by the hardware configuration shown in FIG. Each configuration will be described below.

  The query acquisition unit 401 acquires a search query from the information distribution server 200.

  The content calculation unit 402 searches a content database (not shown) for content corresponding to the search query acquired by the query acquisition unit 401, and determines content to be transmitted. Specifically, the content calculation unit 402 determines content to be transmitted to the information distribution server 200 based on the result of calculation processing for each content searched from the content database. As a specific example of the calculation process, the content calculation unit 402 calculates the number of metadata corresponding to the search query and the like for the content corresponding to the search query.

  The query search result content transmission unit 403 transmits the content determined by the content calculation unit 402 and the ranking information of each content to the information distribution server 200.

(User terminal)
Next, the user terminal 100 will be described. FIG. 13 is a block diagram illustrating a functional configuration of the user terminal 100. As illustrated in FIG. 13, the user terminal 100 includes a browser 101, a recommendation request transmission unit 102, and a distribution information display unit 103. The user terminal 100 is configured by hardware such as a CPU, a RAM, and a ROM, and the CPU is configured to operate according to a stored program. Specifically, it has the same configuration as that of the information distribution server 200 and is realized by the hardware configuration shown in FIG. Each configuration will be described below.

  The browser 101 is an application part for connecting to the Internet, and accesses a site arranged on the Internet and allows a user to view content held on the site.

  The recommendation request transmission unit 102 is a part that transmits a request for recommendation information to the information distribution server 200 together with a search query and a user ID when a user operates an operation unit (not shown).

  The information distribution display unit 103 is a part that displays the menu screen and content (Web page) acquired by the browser 101.

  Next, operations of the user terminal 100, the information distribution server 200, the recommendation server 300, and the search server 400 will be described with reference to FIG. FIG. 14 is a sequence diagram illustrating processing of the user terminal 100, the information distribution server 200, the recommendation server 300, and the search server 400 when the search result is distributed in response to a request from the user terminal 100.

  As shown in FIG. 14, the recommendation request transmission unit 102 of the user terminal 100 transmits a user ID and a search query to the information distribution server 200 and requests a search result transmission (step S1).

  The search request receiving unit 203 of the information distribution server 200 acquires a user ID and a search query and also receives a search result transmission request, and the query search result acquiring unit 206 transmits the search query to the search server 400 (step) S2).

  The recommendation acquisition target content determination unit 204 determines the recommendation acquisition target content that is the content to be recommended acquisition based on the search query at the time of the search request and the content history information stored in the content history information storage unit 201. (Step S3).

  The related content acquisition unit 205 transmits the content ID of the recommendation acquisition target content to the recommendation server 300 (step S4). The related content calculation unit 302 of the recommendation server 300 calculates the related content of the recommendation acquisition target content (step S5), and the related content transmission unit 303 of the recommendation server 300 sends the calculation result of the related content to the information distribution server 200. Transmit (step S6).

  The content calculation unit 402 of the search server 400 calculates content corresponding to the search query (step S7), and the query search result content transmission unit 403 of the search server 400 transmits the result based on the calculation to the information distribution server 200 ( Step S8).

  The content evaluation unit 207 determines the priority of content to be output as a search result based on the content evaluation value acquired by the related content acquisition unit 205 and the content evaluation value acquired by the query search result acquisition unit 206. (Step S9). Then, the content aggregation unit 208 aggregates the content search results after the evaluation by the content evaluation unit 207 for each genre (step S10). Finally, the content search result output unit 209 outputs the content search result after aggregation to the user terminal 100 (step S11). Details of the processes in steps S9 to S11 will be described later.

Next, the evaluation calculation process will be described using the flowchart of FIG. This process corresponds to step S9 in the sequence diagram shown in FIG. First, the content evaluation unit 207 obtains an evaluation value V1 for each content in the content group C1 that is a set of content acquired by the query search result acquisition unit 206 (step S91). The evaluation value V1 calculated by the content evaluation unit 207 can be obtained by the following equation (1).
V1 = (M−α) × {(x + 1−rank ₁ ) / x} (1)

Here, x is the number of contents acquired by the query search result acquisition unit 206, and α is the priority between the content acquired by the related content acquisition unit 205 and the content acquired by the query search result acquisition unit 206. The index value is determined, M is the maximum value that α can take, and rank ₁ is the rank of each content in the content acquired by the query search result acquisition unit 206. Therefore, the evaluation value V1 is a value calculated based on the number of contents acquired by the query search result acquisition unit 206 and the ranking of each content in the content acquired by the query search result acquisition unit 206. If the rank of each content in the content acquired by the query search result acquisition unit 206 is higher, the higher the rank, the higher the evaluation value V1. The content evaluation unit 207 calculates the evaluation value V1 by the above formula (1). However, the content evaluation unit 207 may calculate the evaluation value V1 by other various formulas. For example, in the above formula (1), (x + 1−rank ₁ ) is divided by x, but x may be divided by rank ₁ .

  When there is no content included in the content group C2 that is a set of content acquired by the related content acquisition unit 205 among the content included in the content group C1 (step S92; NO), the content search result output unit 208 evaluates. The contents having the ranking according to the ranking of the value V1 are output (step S93). When the content included in the content group C1 includes content included in the related content group C2 acquired by the related content acquisition unit 205 (step S92; YES), the content evaluation unit 207 displays the content of the content of the related content group C2. Among them, the evaluation value V2 of the content included in the content group C1 is obtained (step S94). Among the contents of the related content group C2, contents not included in the content group C1 are excluded from evaluation. In addition, you may make it obtain | require the evaluation value V2 of the content which is not contained in the content of the content group C1 among the content of the related content group C2.

The evaluation value V2 calculated by the content evaluation unit 207 can be obtained by the following equation (2).
V2 = α × {(x + 1−rank ₂ ) / x} (2)

Here, x is the number of cases acquired by the related content acquisition unit 205, and α is an index value that determines the priority between the content acquired by the related content acquisition unit 205 and the content acquired by the query search result acquisition unit 206 Rank ₂ is the rank of each content in the related content acquired by the related content acquisition unit 205. Therefore, the evaluation value V2 is a value calculated based on the number of contents acquired by the related content acquisition unit 205 and the rank of each content in the content acquired by the related content acquisition unit 205. If the rank of each content in the content acquired by the related content acquisition unit 205 is higher, the higher the rank, the higher the evaluation value V2. The content evaluation unit 207 calculates the evaluation value V2 by the above formula (2). However, the content evaluation unit 207 may calculate the evaluation value V2 by other various formulas. For example, in the above formula (2), (x + 1−rank ₂ ) is divided by x, but x may be divided by rank ₂ .

Subsequently, the content evaluation unit 207 obtains a final evaluation value V3 from the evaluation values V1 and V2 (step S95). The final evaluation value V3 can be obtained by the following equation (3). In this way, by calculating the final evaluation value using the evaluation value V1 in the content acquired by the related content acquisition unit 205 and the evaluation value V2 in the content acquired by the query search result acquisition unit 206. Evaluation values for a plurality of viewpoints can be calculated, and appropriate content evaluation values can be calculated.
V3 = V1 + V2 (3)

  Next, content aggregation processing will be described using the flowchart of FIG. This process corresponds to step S10 and step S11 in the sequence diagram shown in FIG.

  First, the content aggregation unit 208 determines the genre related to the content having the highest evaluation value V3 calculated by the content evaluation unit 207 as a reference genre (step S101). After determining the reference genre, the content aggregating unit 208 obtains the number of search results after aggregation for each genre based on the relevance table (see FIG. 9) stored in the genre structure storage unit 210 (step S102). Specifically, the degree of association (attenuation constant) of each genre is specified from the identifier of the reference genre, the identifier of each genre, and the degree of association table, and the number of reference results N, which is the number of search results of the reference genre, The number of search results for each genre is determined by multiplying the degree of relevance.

  Then, the content aggregating unit 208 aggregates the content for each genre (step S103). Specifically, the content aggregating unit 208 performs an aggregation process of content search results for each genre so that the number of search results determined for each genre is obtained. Note that the content aggregating unit 208 preferentially selects content having a high evaluation value calculated by the above-described content evaluation unit 207 as content to be a content search result after aggregation.

  Finally, the content search result output unit 209 outputs the content search result aggregated by the content aggregation unit 208 to the user terminal 100 (step S104).

  Next, the effect of the information delivery server 200 of this embodiment is demonstrated. According to the information distribution server 200 of the present embodiment, the content search result for the search query is aggregated for each predetermined similar range by the content aggregation unit 208, and the content search result after the aggregation is output by the content search result output unit 209. The Thereby, the content search results after being aggregated for each predetermined similar range are displayed on the user terminal 100, and the number of similar content displayed is reduced. Since the number of similar contents to be displayed is reduced, contents that could not be displayed on the user terminal 100 (one screen) are displayed. That is, various types of content are easily displayed as compared to the conventional case. As a result, the user can more easily confirm various types of content as search results than before. In addition, since various types of content are easily displayed, it can be expected that the serendipity of the user is improved.

  In the information distribution server 200 according to the present embodiment, the content aggregating unit 208 appropriately aggregates content having similar characteristics by aggregating content search results with a genre indicating the type of each content as a predetermined similarity range. It becomes possible.

  In the information distribution server 200 of the present embodiment, the content aggregating unit 208 determines the number of search results according to the importance for each genre, for example, the degree of association with the reference genre, and aggregates the content search results. Various types of content display can be performed while displaying a large number of contents of a genre having a high importance for the user.

  The information distribution server 200 of the present embodiment further includes a genre structure storage unit 210 that stores a genre structure in which each genre is hierarchically structured, and the content aggregation unit 208 includes a genre structure stored in the genre structure storage unit 210. Based on the above, the number of search results for each genre is determined, and the content search results are aggregated. Since the degree of relevance between each genre can be easily and appropriately defined by the genre structure, the number of search results for each genre can be easily determined, and the content search results can be easily aggregated.

  Further, in the information distribution server 200 of the present embodiment, the content aggregation unit 208 determines a reference genre serving as a reference among the genres, and based on an identifier that specifies the reference genre and an identifier that specifies each genre, Determine the number of genre search results and aggregate content search results. By determining the reference genre and determining the number of search results for each genre from the degree of association with the reference genre, the content search results for each genre can be more easily aggregated. Further, the number of search results for each genre is determined by specifying the degree of association (attenuation constant) of each genre from the identifier of the reference genre, the identifier of each genre, and the degree of association table, and determining the number of search results for each genre. This facilitates the aggregation of content search results.

  The information distribution server 200 of the present embodiment further includes a content evaluation unit 207 that calculates an evaluation value of each content included in the content search result, and the content aggregation unit 208 includes the content search result calculated by the content evaluation unit 207. By determining the reference genre based on the evaluation value, the genre related to the content desired by the user can be set as the reference genre. Since the number of search results for each genre is determined based on the degree of association with the reference genre desired by the user, various types of content can be displayed while displaying many contents of the genre desired by the user. Become.

  The preferred embodiments of the present invention have been described above. However, the present invention is not limited to the above-described embodiments. The present invention is modified without departing from the scope described in the claims or applied to others. It may be.

  For example, the genre structure storage unit 210 has been described as storing hierarchically structured genres, but the genres may not necessarily be hierarchically structured, and the relationship (relationship) between each genre may be It is only necessary to store the genre so that it can be identified.

  Although the reference genre has been described as being determined based on the evaluation value calculated by the content evaluation unit 207, a genre related to content that the user holds as a history may be simply used as the reference genre.

DESCRIPTION OF SYMBOLS 200 ... Information distribution server, 205 ... Related content acquisition part, 206 ... Query search result acquisition part, 207 ... Content evaluation part, 208 ... Content aggregation part, 209 ... Content search result output part, 210 ... Genre structure storage part.

Claims (7)

Search result acquisition means for acquiring a plurality of content search results for a search query;
Content aggregation means for aggregating the content search results acquired by the search result acquisition means for each predetermined similar range;
Output means for outputting the content search results aggregated by the content aggregation means;
A content search result providing apparatus comprising:   The content search result providing apparatus according to claim 1, wherein the content aggregation unit aggregates content search results with a genre indicating a type of each content as the predetermined similarity range.   The content search result providing apparatus according to claim 1 or 2, wherein the content aggregation means determines the number of search results according to the importance for each of the predetermined similar ranges, and aggregates the content search results. Similar range structure storage means for storing a similar range structure in which a plurality of the predetermined similar ranges are hierarchically structured,
The content search result according to any one of claims 1 to 3, wherein the content aggregation unit aggregates the content search results of each similar range based on the similar range structure stored in the similar range structure storage unit. Providing device.   The content aggregating unit determines a reference similar range as a reference among the similar ranges, and aggregates content search results of the similar ranges based on an identifier that identifies the reference similar range and an identifier that identifies each similar range. The content search result provision apparatus as described in any one of Claims 1-4. A content evaluation unit that calculates an evaluation value of each content included in the content search result acquired by the search result acquisition unit;
The content search result providing apparatus according to claim 5, wherein the content aggregation unit determines the reference similarity range based on an evaluation value of the content search result calculated by the content evaluation unit. A search result acquisition step for acquiring a plurality of content search results for the search query;
A content aggregation step of aggregating the content search results acquired in the search result acquisition step for each predetermined similar range;
An output step of outputting the content search results aggregated in the content aggregation step;
Content search result providing method including

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