JP5882372B2 - Information processing apparatus and program - Google Patents

Description

  The present invention relates to a technology for displaying content.

  2. Description of the Related Art In recent years, there are information processing apparatuses that preferentially display content that satisfies a predetermined condition from various content existing in a network on a display unit. Examples of the content include video, images, music, articles, advertisements, and various information obtained by combining them.

  For example, Patent Document 1 (Japanese Patent Laid-Open No. 2008-176491) discloses a technique that allows a user to browse information of personal interest and information of a category with high public interest.

  In Patent Document 1, first, a heading information group is fetched from the Internet, each heading information is classified into a plurality of categories, and stored in a category-specific database. Then, based on the interest degree database, the heading information of the category having the interest degree equal to or higher than a predetermined reference value is extracted, and a list screen is created and displayed. Further, the degree of interest is learned by updating the degree of interest of each category according to the user's selection operation on the list screen.

JP 2008-176491 A

  When content belonging to any of a plurality of categories is displayed for each category, not only content with high personal preference of the user is preferentially displayed but also content with high social preference is given priority. It is also important to display.

  This is because when content is displayed for each category, content with a high degree of personal preference of the user is preferentially displayed. For categories that the user dislikes, the user's personal preference is learned. This is because the speed is slow and the user's preference cannot be specified with high accuracy, so that appropriate content cannot be displayed to the user.

  The personal preference level is a value serving as an index for specifying the degree of preference of the user's personal content, and is obtained from the user's personal evaluation index. Content with a high personal preference means that the user is interested in the content, and content with a low personal preference means that the user is not interested in the content. The social preference level is a value that serves as an index for specifying the degree of relative preference with respect to content of a plurality of users, and is obtained from an evaluation index of a plurality of users. Content with a high social preference level means that a plurality of users are relatively interested in the content and have a high level of social attention. Content with a low social preference level means that a plurality of users are relatively uninterested in the content and have a low level of social attention.

  An object of the present disclosure is to display content appropriate for a user when content belonging to one of a plurality of categories is displayed for each category.

An information processing apparatus according to an aspect of the present disclosure is provided.
An information processing apparatus that displays content belonging to any of a plurality of categories on a display unit for each category,
First acquisition means for acquiring a first ranking in which the social preference of each content for each category obtained by an evaluation index of a plurality of users is ranked for each category;
Second acquisition means for acquiring a second ranking in which the personal preference of each content common to the categories obtained by an evaluation index of a user who operates the information processing apparatus is ranked in common with the categories;
A fusion means for creating an integrated ranking in which the preference of each content is ranked by combining the first ranking and the second ranking;
Based on the integrated ranking, display control means for selecting the content to be displayed for each category so that the display probability of content with a high rank is high;
It is characterized by having.

  According to one aspect of the present disclosure, when content belonging to any of a plurality of categories is displayed for each category, appropriate content can be displayed to the user.

It is a figure which shows the structural example of the information processing apparatus 100 of 1st Embodiment. It is a figure which shows an example of an information source table. It is a figure showing an example of processing operation of information processor 100 of a 1st embodiment. It is a figure which shows the preference order example of each content for every category. It is a 1st figure which shows an example of the relationship between a ranking and a content score. It is a 2nd figure which shows an example of the relationship between a ranking and a content score. It is a figure which shows the structural example of the information processing apparatus 100 of 4th Embodiment. It is a figure which shows the processing operation example of the information processing apparatus 100 of 4th Embodiment.

(Outline of information processing apparatus 100 according to one aspect of the present disclosure)
First, an information processing apparatus 100 according to an aspect of the present disclosure will be described with reference to FIG. FIG. 1 illustrates a configuration example of an information processing apparatus 100 according to an aspect of the present disclosure.

  An information processing apparatus 100 according to an aspect of the present disclosure is an information processing apparatus that displays content belonging to any of a plurality of categories on a display unit for each category. The display unit 1 functions as a display means.

  The information processing apparatus 100 according to an aspect of the present disclosure includes a first acquisition unit, a second acquisition unit, a fusion unit, and a display control unit.

  A 1st acquisition means acquires the 1st ranking which ranked the social preference degree of each content for every category obtained from the evaluation index of a plurality of users for every category. The social preference level detection unit 41 functions as the first acquisition means.

  The second acquisition unit acquires a second ranking obtained by ranking the personal preference of each content common to the categories obtained by the evaluation index of the user who operates the information processing apparatus 100 in the category common. As the second acquisition means, the personal preference level detection unit 42 functions.

  The merging means creates an integrated ranking in which the preference of each content is ranked by combining the first ranking and the second ranking. The preference level fusion unit 43 functions as a fusion means.

  Based on the integrated ranking, the display control means selects the content to be displayed for each category so that the display probability of the high-ranking content is high. The display control unit 44 functions as the display control means.

  The information processing apparatus 100 according to an aspect of the present disclosure includes a first ranking in which the social preference of each content for each category is ranked for each category, and the personal preference of each content common to the categories in the category An integrated ranking is created by combining the second ranking commonly ranked and ranking the preference of each content. Then, based on the integrated ranking, the content to be displayed for each category is selected so that the display probability of the content with the higher rank is increased.

  Accordingly, the information processing apparatus 100 according to an aspect of the present disclosure may easily display content having a high social preference with priority based on the personal preference for a category that the user dislikes. it can. In addition, regarding a user's favorite category, content with a high personal preference can be preferentially displayed based on the social preference. As a result, when content belonging to any of a plurality of categories is displayed for each category, appropriate content can be displayed to the user. Hereinafter, an embodiment of an information processing apparatus 100 according to an aspect of the present disclosure will be described in detail with reference to the accompanying drawings.

(First embodiment)
<Configuration example of information processing apparatus 100>
First, a configuration example of the information processing apparatus 100 according to the present embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating a configuration example of an information processing apparatus 100 according to the present embodiment. Examples of the information processing apparatus 100 include various devices such as a straight type PC, a tablet type PC, and a smartphone.

  The information processing apparatus 100 according to the present embodiment includes a display unit 1, an operation unit 2, a storage unit 3, and a control unit 4.

  The display unit 1 displays various information. In the present embodiment, content is displayed. Examples of the content include video, images, music, articles, advertisements, and various information obtained by combining them.

  The operation unit 2 inputs various information. In the present embodiment, for example, various information for controlling the information processing apparatus 100 is input.

  The storage unit 3 stores various information. The storage unit 3 of this embodiment has a content storage unit 31. The content storage unit 31 acquires various contents stored in a server or the like on the network, and classifies and stores them for each predetermined category. The content storage unit 31 stores, for example, content acquired from a server on the Internet based on an information source table as shown in FIG. In the information source table shown in FIG. 2, categories such as “politics”, “economy”, and “sports” are associated with each URI. The content is like each of the articles included in the RSS or Atom feed acquired based on this URI. As a result, various contents of various categories are stored in the content storage unit 31.

  The control unit 4 controls the information processing apparatus 100. The control unit 4 of the present embodiment includes a social preference level detection unit 41, a personal preference level detection unit 42, a preference level fusion unit 43, and a display control unit 44.

  The social preference level detection part 41 calculates | requires the social preference level of each content for every category. The content storage unit 31 stores various contents classified into predetermined categories. The social preference level detection unit 41 obtains the social preference level of various contents stored in the content storage unit 31 for each category, and creates a ranking in which the social preference level of each content is ranked for each category. Thereby, it is possible to grasp which content has a high social preference level and which content has a low social preference level in each category. The social preference level is a value that serves as an index for specifying the degree of relative preference with respect to content of a plurality of users, and is obtained from an evaluation index of the plurality of users. The social preference may be a score, a deviation value, a ranking for the content, or a combination thereof. Content with a high social preference level means that a plurality of users are relatively interested in the content and have a high level of social attention. Content with a low social preference level means that a plurality of users are relatively uninterested in the content and have a low level of social attention. The social preference level is determined based on, for example, the number of tweets for the content. Also, it is determined based on the number of users who liked the content. Since the social preference of content for each category is obtained in real time by a plurality of users, the learning speed is fast.

  The personal preference level detection unit 42 obtains the personal preference level of each content common to the categories. The content storage unit 31 stores various contents classified into predetermined categories. The personal preference level detection unit 42 obtains the personal preference level of various contents stored in the content storage unit 31 for each category, not for each category, and ranks the personal preference level of each content for each category. Create Thereby, it is possible to grasp which content has a high personal preference among all categories and which content has a low personal preference regardless of the category. The personal preference level is a value serving as an index for specifying the degree of preference of the user's personal content, and is obtained from the user's personal evaluation index. The personal preference may be a score, a deviation value, a rank for the content, or a combination thereof. Content with a high personal preference means that the user is interested in the content, and content with a low personal preference means that the user is not interested in the content. The personal preference level is determined using, for example, a user operation on the content displayed on the display unit 1 and a keyword included in the content. Examples of user operations include opening the content, not opening the content, making a good comment on the content, making a bad comment on the content, saving the content, and discarding the content. It is done. Among these user operations, operations such as opening the content, making a good comment on the content, saving the content, and the like correspond to operations showing a positive correlation with the personal preference. In addition, operations such as not opening the content, making a bad comment on the content, or discarding the content correspond to operations indicating a personal preference and a negative operation. As a result, the content including the same keyword as the keyword included in the content that has been operated to have a positive correlation with the personal preference level has a high personal preference level, and an operation having a negative correlation with the personal preference level. The content including the same keyword as the keyword included in the content that has been performed has a low personal preference. In addition, based on the keywords included in the content, rather than obtaining the personal preference level of the content common to the categories, items that are more detailed than the category for which the social preference level is obtained and that span multiple categories in common ( For example, it is also possible to classify one category in more detail and obtain the personal preference of content common to the categories by subcategories across a plurality of categories. Since the personal preference of content common to categories is obtained by user operation, the learning speed is slow.

  The preference level fusion unit 43 ranks the social preference level of each content obtained by the social preference level detection unit 41 and the personal preference of each content common to the categories obtained by the personal preference level detection unit 42. The ranking of each content is combined, and the integrated ranking of the degree of preference of each content is created by merging the degree of social preference and personal preference. Thereby, for example, even if the content has a high social preference level, if the personal preference level is low, it is possible to rank the content by lowering the relative preference level. Moreover, even if the content has a low social preference level, if the personal preference level is high, the content can be ranked by increasing the relative preference level.

  The display control unit 44 selects the display target content to be displayed on the display unit 1 based on the integrated ranking of the preference level of each content created by the preference level fusion unit 43 and displays the content on the display unit 1.

<Example of Processing Operation of Information Processing Device 100>
Next, an example of processing operation of the information processing apparatus 100 according to the present embodiment will be described with reference to FIG. FIG. 3 is a diagram illustrating a processing operation example of the information processing apparatus 100 according to the present embodiment.

  First, the content storage unit 31 acquires various contents stored in a server or the like on the network, and classifies and stores them for each predetermined category (step S1).

  Next, the social preference level detection part 41 calculates | requires the social preference level of each content for every category, and produces the ranking which ranked the social preference level of each content for every category for every category (step S2). . For example, as shown in FIG. 4, the preference order (= Rs20) of each content for each category is specified based on the number of tweets for the content. The preference order is determined from 1st to 20th. In this case, the content is ranked in order from the content with the largest number of tweets for each category. In the present embodiment, the preference order of each content for each category is determined from 1 to 20th, and the rankings of 21st and lower are all the same. For this reason, all the remaining contents other than the 1st to 20th ranks for each category are ranked 21st.

  Further, the personal preference level detection unit 42 obtains the personal preference level of each content common to the category, and creates a ranking in which the personal preference level of each content common to the category is ranked common to the category (step S3). For example, the preference order (= Ri) of each content common to the categories is specified using a technique such as naive Bayes or support vector machine. Then, the contents of the top 1/4 of Ri are assigned to 1 to 20 (Ri20). For example, if Ri is 160, the upper quarter of Ri is 40. For this reason, the content of the preference ranking 1 to 40 is assigned to the 1st to 20th. In this case, the first content of Ri20 distributes the two contents of the preference rankings 1 and 2, and the second content of Ri20 distributes the two contents of the preference rankings 3 and 4. For this reason, two contents are distributed to each rank of Ri20. In addition, all the contents other than the ranking of the upper quarter of Ri are ranked 21st.

  For example, if all contents are classified as likes, neither, or dislikes, the top 1/4 content is expected to be classified as likes. In addition, the lower 1/4 content is expected to be disliked. In Naive Bayes, the score has a negative value and a positive value, the negative value is evaluated as disliked, and the positive value is evaluated as liked. In the present embodiment, if a negative score content is present in the top 1/4 content, the negative score content is excluded. In this case, the remaining content excluding the negative score content is assigned to the first to twentieth positions. It is also possible to make up for the number of contents with a negative score from the lower contents other than the upper 1/4 contents and distribute them to the first to twentieth positions. It is also possible to exclude the negatively-scoring content after the top 1/4 content has been assigned to 1-20.

  Next, the preference level fusion unit 43 ranks the social preference level of each content obtained by the social preference level detection unit 41 and the content common to the categories obtained by the personal preference level detection unit 42. The ranking of the personal preference is combined, and the integrated ranking of the preference of each content is created by combining the social preference and the personal preference (step S4).

  In the present embodiment, the ranking of the social preference level of each content for each category is Rs20, and the ranking of the personal preference level of each content common to the category is Ri20. For this reason, based on Rs20 and Ri20, the integrated ranking (Rc20) of the preference level of each content obtained by merging Rs20 and Ri20 is obtained. For example, Rs20 and Ri20 are arithmetically averaged, and the integrated ranking (Rc20) of the preference level of each content is obtained by the following equation.

  Rc20 = (Rs20 + Ri20) / 2

  According to the above formula, for example, the content ranking (Rc20) of the content in which Rs20 is first and Ri20 is eleventh is sixth. Further, the content ranking (Rc20) of the content in which Rs20 is 11th and Ri20 is 1st is 6th. Further, the content ranking (Rc20) of the content in which Rs20 is 21st and Ri20 is 21st is 21st.

  Next, the display control unit 44 selects and displays the display target content to be displayed on the display unit 1 based on the integrated ranking (Rc20) created by the preference level fusion unit 43 (step S5). . The display control unit 44 calculates a content score corresponding to the ranking order based on the integrated ranking (Rc20) of the preference level of each content. As an example, in the present embodiment, the content score Qj is calculated by the following formula.

Qj = (w ^ (1-rank)) × 1000
w: Ranking score coefficient (1.0 <w)
rank: Ranking ranking (1 ≦ rank)

  When the content score Qj is calculated by the above formula, the relationship between the integrated ranking (Rc20) and the content score can be calculated as shown in FIGS. As shown in the figure, the content ranking is calculated so that the content score is higher as the integrated ranking (Rc20) is higher.

  The display control unit 44 selects content at random with a frequency according to the calculated content score ratio and displays the content on the display unit 1. For example, first, the sum S of content scores (Ci) is calculated. Note that the subscript i has a one-to-one correspondence with the content.

  S = Σ (Ci)

  Next, a random number R (0 ≦ R <S) is generated, and the content indicated by the smallest i satisfying the following conditions is set as the content to be selected.

R ≦ Si, Si = Σ (Ci)
However, Si is the sum total from C0 to Ci.

  When content is selected by the above method, it is possible to select content randomly at a frequency according to the content score ratio of each content. The display control unit 44 sequentially displays the selected content on the screen of the display unit 1 when displaying the content for each category.

  The information processing apparatus 100 according to the present embodiment performs the processing operation illustrated in FIG. 3, so that when content is displayed for each category on the display unit 1, even if the content has high social preference within the category, It is possible to prevent content with a low personal preference from being displayed as much as possible. Further, since the information processing apparatus 100 according to the present embodiment obtains the personal preference level of each content common to the categories, when displaying the content for each category, the personal preference level is reflected in the content of all categories. Can be made. For example, when the personal preference level of each content for each category is obtained, the user's favorite category is frequently displayed on the display unit 1 and the number of samples is large, so the learning rate of the personal preference level is high. In addition, the personal preference can be obtained with high accuracy. However, the category that the user dislikes is not easily displayed on the display unit 1 and the number of samples is small, so the learning speed of the personal preference is slow and the personal preference cannot be obtained with high accuracy. become. As a result, for categories that the user dislikes, it is impossible to display appropriate content for the user.

  On the other hand, in this embodiment, since the personal preference degree of each content common to a category is calculated | required, the personal preference degree can be reflected with respect to the content of all the categories. As a result, with respect to categories that the user dislikes, it is possible to preferentially display content with a high degree of social preference. In addition, for a user's favorite category, content with a high personal preference can be easily displayed preferentially. For this reason, it is possible to display appropriate content for the user.

  In the above processing operation example, the integrated ranking (Rc20) is obtained using an arithmetic average. However, it is also possible to obtain using other methods such as weighted average, harmonic average, and geometric average. The arithmetic mean is obtained by the following equation (1). In the above processing operation example, the integrated ranking (Rc20 = μ) is obtained in the case of n = 2 in the following equation (1). The following equation (2) is a weighted average calculation equation, equation (3) is a harmonic average calculation equation, and equation (4) is a geometric mean calculation equation.

In general, for a positive value, the arithmetic average ≧ geometric average ≧ harmonic average, and for the 21st and lower ranked contents that are not ranked, the ranked 1st to 20th ranked contents are: The harmonic average is most easily selected as the content to be displayed. The weighted average can be used to prioritize the social preference level or the personal preference level, and can adjust the degree of preference. The degree of preference can be adjusted by changing the weighting of w _n in equation (2). In order to control the display probability of the content displayed for each category, the degree of preference may be adjusted.

<Operation / Effect of Information Processing Apparatus 100 of Present Embodiment>
As described above, the information processing apparatus 100 according to the present embodiment creates a ranking (Rs20) in which the social preference of each content for each category is ranked for each category. Also, a ranking (Ri20) is created in which the personal preference of each content common to the categories is ranked common to the categories. Then, the ranking (Rs20) ranked for each category and the ranking (Ri20) ranked common to the categories are combined to create an integrated ranking (Rc20) that ranks the degree of preference of each content. Then, based on the created integrated ranking (Rc20), the content to be displayed is selected and displayed on the display unit 1. Thereby, an appropriate content can be displayed to a user.

  In addition, the information processing apparatus 100 according to the present embodiment creates a personal preference ranking (Ri20) of each content common to the categories by using the top 1/4 content having a high personal preference. Therefore, it is possible to create an integrated ranking (Rc20) that reflects only content with a high personal preference.

  In addition, the information processing apparatus 100 according to the present embodiment normalizes the personal preference by assigning the top 1/4 content with the highest personal preference to 1 to 20 places. For this reason, the weighting of personal preference and social preference can be made the same.

(Second Embodiment)
Next, a second embodiment will be described.

  In the first embodiment, the personal preference level detection unit 42 assigns 1 to 20 in the case of allocating the content of the top 1/4 of the preference ranking (= Ri) of each content common to the categories to 1 to 20th. Content is allocated to all ranks at the same rate. Specifically, in the above-described embodiment, 40 contents, which are the upper 1/4 of 160, are distributed from the first place to the 20th place two by two. However, instead of allocating content to all ranks 1 to 20 at the same rate, content with personal preference is allocated in accordance with the proportion of the number of contents in each ranking of social preference. It is also possible.

  For example, if the number of content in the first place is 50, the number of content in the 10th place is 25, and there are a total of 500 contents from the first to the 20th place in the social preference, The ratio of the number of contents is 50/500 = 10%. In this case, if the number of content in the top 1/4 of the category common content preference rank (= Ri) is 40, 10% of 40 is 4, and is assigned to the first personal preference ranking. Makes four contents. Similarly, the ratio of the number of contents at the 10th place is 25/500 = 5%, 5% of 40 is 2, and the contents assigned to the 10th place in the personal preference ranking are two contents. Thereby, the influence of the personal preference degree on the social preference degree can be made uniform.

(Third embodiment)
Next, a third embodiment will be described.

  In the first embodiment, a ranking (Rs20) in which the social preference level of each content for each category is ranked for each category, and a ranking (Ri20) in which the personal preference level of each content common to the category is ranked in common to the categories. ), And an integrated ranking (Rc20) that ranks the preference of each content. The social preference level of each content for each category used in the first embodiment is obtained by an evaluation index of an unspecified number of users, and the preference level of a user having a profile different from that of a user having a personal preference level It is included. Profiles include sex (male, female, etc.), age (10's, 20's, 30's, etc.), height (150-160cm, 160-170cm, 170-180cm, etc.), educational background (high school, undergraduate, graduate) Etc.).

  For this reason, the population of the social preference of each content for each category is further classified by profile (gender, age, weight, height, educational background, etc.). And the social preference degree of each content for every category corresponding to the user profile of the personal preference degree is specified. And, the ranking that ranks the social preference of each content for each specified category for each category and the ranking that ranks the personal preference of content common to all categories in common (Ri20) It is also possible to create an integrated ranking that ranks the degree of preference of each content. For example, when the profile of the user of the personal preference is a male in their 30s, the ranking in which the social preference of each content for each category corresponding to the male profile is ranked for each category, and the profile in the 30s A combination of a ranking that ranks the social preference of each content for each category in each category and a ranking that ranks the personal preference of content common to all categories (Ri20). Create an integrated ranking that ranks the personal preference of. This makes it possible to recommend more optimal content to the user. This method is suitable when the learning of the social preference level of each content corresponding to the category corresponding to the user profile of the personal preference level has matured.

  In addition, the ranking that ranks the social preference of each content for each category corresponding to the user's profile of personal preference for each category and the social preference of each content for each category for each category Combining the ranking (Rs20) and the ranking (Ri20) that ranks the personal preference of each content common to the category in common, it is also possible to create an integrated ranking that ranks the preference of each content It is. For example, when the profile of the user of the personal preference is a male in their 30s, the ranking in which the social preference of each content for each category corresponding to the male profile is ranked for each category, and the profile in the 30s Ranking that ranks social preference of each content for each category in each category, ranking that ranks social preference of each content in each category for each category (Rs20), and content common to each category Is combined with the ranking (Ri20) that ranks the personal preference of each category in common with each category to create an integrated ranking that ranks the personal preference of each content. This makes it possible to recommend more optimal content to the user. This method is suitable for the initial stage where learning of the social preference level of each content corresponding to the category corresponding to the user profile of the personal preference level is not mature.

(Fourth embodiment)
Next, a fourth embodiment will be described.

  In the first embodiment, as illustrated in FIG. 1, the information processing apparatus 100 includes a social preference level detection unit 41, and sets the social preference level of each content for each category as illustrated in FIG. 4 for each category. It is decided to create a ranking (Rs20) with ranking. However, as shown in FIG. 7, it is also possible to have a social preference level acquisition unit 45 and acquire information on the ranking (Rs20) as shown in FIG. 4 from the server 200 of the network. Since the social preference level is obtained from the evaluation indexes of a plurality of users, it is not performed by the information processing apparatus 100, but is performed by the server 200 that easily obtains the evaluation indexes of a plurality of users, thereby efficiently ranking (Rs20 ) Can be created. FIG. 7 is a diagram illustrating a configuration example of the information processing apparatus 100 according to the fourth embodiment.

  In this embodiment, it has the server 200, and the server 200 has the memory | storage part 5 which has the content storage part 51, and the control part 6 which has the social preference degree detection part 61, and is comprised.

  The content accumulation unit 51 acquires various contents stored in a server on the network, classifies and accumulates them for each predetermined category. For example, the content storage unit 51 stores content acquired from a server on the Internet based on an information source table as shown in FIG. The content storage unit 31 of the information processing apparatus 100 according to the present embodiment acquires various contents stored in the content storage unit 51 of the server 200, classifies and stores the various contents.

  The social preference level detection unit 61 calculates the social preference level of various contents stored in the content storage unit 51 for each category, and creates a ranking (Rs20) that ranks the social preference level of each content for each category. To do. The social preference level acquisition unit 45 acquires the ranking (Rs20) created by the social preference level detection unit 61 of the server 200.

<Example of Processing Operation of Information Processing Device 100>
Next, an example of processing operation of the information processing apparatus 100 according to the present embodiment will be described with reference to FIG. FIG. 8 is a diagram illustrating a processing operation example of the information processing apparatus 100 according to the present embodiment.

  First, the content accumulating unit 31 acquires various contents accumulated in the content accumulating unit 51 of the server 200, classifies and accumulates them for each category (Step S11).

  Next, the social preference level acquisition unit 45 acquires the ranking (Rs20) in which the social preference level of each content for each category is ranked for each category from the social preference level detection unit 61 of the server 200 (step S12). ).

  In addition, the personal preference level detection unit 42 obtains the personal preference level of each content common to the categories and ranks the personal preference level of each content common to the categories in the same manner as in the first embodiment. A ranking (Ri20) is created (step S13).

  Next, the preference fusion unit 43 shares the social preference ranking (Rs20) of each content for each category acquired by the social preference acquisition unit 45 and the category common obtained by the personal preference detection unit 42. The ranking of personal preference of each content (Ri20) is combined to create an integrated ranking (Rc20) of the preference of each content that combines the social preference and personal preference (step S14).

  Next, the display control unit 44 selects and displays the display target content to be displayed on the display unit 1 based on the integrated ranking (Rc20) created by the preference level fusion unit 43 (step S15). .

  The information processing apparatus 100 according to the present embodiment acquires, from the server 200, a ranking (Rs20) in which the social preference level of each content for each category is ranked for each category. As a result, the processing load performed by the information processing apparatus 100 can be reduced, and the ranking (Rs20) efficiently created by the server 200 can be efficiently acquired.

  The above-described embodiment is a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above-described embodiment alone, and various modifications are made without departing from the gist of the present invention. Implementation is possible.

  For example, when content belonging to one of a plurality of categories is displayed for each category, content having at least one of the social preference level of the content for each category and the personal preference level of content common to the categories is within the top five It is also possible to control to always display.

  Also, the personal preference ranking can be assigned from the first place, the social preference ranking can be assigned from the fifth place, and the personal preference degree can be controlled to give priority to the social preference degree. .

  In addition, the control operation in each unit constituting the information processing apparatus 100 of the above embodiment can be executed using hardware, software, or a combined configuration of both.

  In the case of executing processing using software, it is possible to install and execute a program in which a processing sequence is recorded in a memory in a computer incorporated in dedicated hardware. Alternatively, it can be installed in a memory in a general-purpose computer capable of executing various processes and executed.

  For example, the program can be recorded in advance on a hard disk or ROM (Read Only Memory) as a recording medium. Alternatively, the program can be stored (recorded) temporarily or permanently in a removable recording medium. Such a removable recording medium can be provided as so-called package software. Examples of the removable recording medium include various recording media such as a magnetic disk and a semiconductor memory.

  The program is installed in the computer from the removable recording medium as described above. In addition, it is wirelessly transferred from the download site to the computer. In addition, it is transferred to a computer via a network by wire.

  In addition, each unit configuring the information processing apparatus 100 of the above embodiment is not limited to performing the processing in time series according to the processing operation described in the above embodiment. For example, it is possible to construct the processing capability of a device that executes processing, or to execute processing in parallel or individually as necessary.

DESCRIPTION OF SYMBOLS 100 Information processing apparatus 1 Display part 2 Operation part 3 Storage part 31 Content storage part 4 Control part 41 Social preference degree detection part 42 Personal preference degree detection part 43 Preference degree fusion part 44 Display control part 45 Social preference degree acquisition part 200 server 5 storage unit 51 content storage unit 6 control unit 61 social preference level detection unit

Claims (6)

An information processing apparatus that displays content belonging to any of a plurality of categories on a display unit for each category,
First acquisition means for acquiring a first ranking in which the social preference of each content for each category obtained by an evaluation index of a plurality of users is ranked for each category;
Second acquisition means for acquiring a second ranking in which the personal preference of each content common to the categories obtained by an evaluation index of a user who operates the information processing apparatus is ranked in common with the categories;
A fusion means for creating an integrated ranking in which the preference of each content is ranked by combining the first ranking and the second ranking;
Based on the integrated ranking, display control means for selecting the content to be displayed for each category so that the display probability of content with a high rank is high;
An information processing apparatus comprising: A ranking obtained by assigning and ranking the top predetermined number of contents with high personal preference of each content common to the categories obtained by the evaluation index of the user who operates the information processing apparatus to the predetermined number of ranks common to the categories. Has creation means to create,
The information processing apparatus according to claim 1, wherein the second acquisition unit acquires the ranking created by the creation unit as the second ranking.   The creation unit adjusts the number of contents allocated to each rank of the predetermined rank number according to a ratio of the number of contents of each rank ranked in the first ranking. 2. The information processing apparatus according to 2. The first acquisition means includes
Obtaining a first ranking in which the social preference of each content for each category obtained by an evaluation index of a plurality of users in a profile common to the user who operates the information processing apparatus is ranked for each category; The information processing apparatus according to claim 1, wherein the information processing apparatus is an information processing apparatus. A third ranking is obtained by ranking the social preference of each content for each category obtained by the evaluation index of a plurality of users having a common profile with the user operating the information processing apparatus for each category. 3 acquisition means,
The said fusion | melting means calculates | requires the integrated ranking which ranked the preference degree of each content combining the said 1st ranking, the said 2nd ranking, and the said 3rd ranking. Item 4. The information processing device according to any one of items 3 to 4. A program for causing a computer to display content belonging to any one of a plurality of categories on a display unit for each category,
A first acquisition process for acquiring a first ranking in which the social preference of each content for each category obtained by an evaluation index of a plurality of users is ranked for each category;
A second acquisition process for acquiring a second ranking in which the personal preference of each content common to the categories obtained by an evaluation index of a user operating the computer is ranked in common with the categories;
A fusion process for creating an integrated ranking in which the preference of each content is ranked by combining the first ranking and the second ranking;
A program that causes the computer to execute display control processing for selecting the content to be displayed for each category based on the integrated ranking so that the display probability of content with a high ranking is increased.

Images