CONTENT RECOMMENDATION SYSTEM, CONTENT RECOMMENDATION METHOD, CONTENT RECOMMENDATION DEVICE, AND INFORMATION STORAGE MEDIUM

• US 20120096026A1
• Filed: 06/08/2010
• Published: 04/19/2012
• Est. Priority Date: 07/14/2009
• Status: Active Grant

• Current Assignee: Sony Corporation (Sony Group Corp.)
• Original Assignee: Sony Corporation (Sony Group Corp.)
• Inventors: Saito, Katsu, Isozaki, Masaaki, Onogi, Wataru, Ishii, Kazuo, Ikeda, Nozomu, Takahashi, Yoshikazu
• Granted Patent: US 8,838,641 B2
• Time in Patent Office: Days
• US Class Current: 707/769
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/435 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/4387 by the use of playlists
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

The content recommendation system includes a user attribute information acquisition means for acquiring attribute information of a given user, a list acquisition means for acquiring at least one list from among lists of content sequentially generated over time on the basis of the attribute information of the given user acquired by the user attribute information acquisition means, a user preference information acquisition means for acquiring user preference information, which is feature information of content preferred by the given user, a content extraction means for extracting some content from content included in the list acquired by the list acquisition means on the basis of the user preference information acquired by the user preference information acquisition means, and a content presentation means for presenting the content extracted by the content extraction means to the given user.

Claims

1. A content recommendation system characterized in comprising:

a user attribute information acquisition means for acquiring attribute information of a given user;

a list acquisition means for acquiring at least one list from among lists of content sequentially generated over time on the basis of the attribute information of the given user acquired by the user attribute information acquisition means;

a user preference information acquisition means for acquiring user preference information, which is feature information of content preferred by the given user;

a content extraction means for extracting some content from content included in the list acquired by the list acquisition means on the basis of the user preference information acquired by the user preference information acquisition means; and

a content presentation means for presenting the content extracted by the content extraction means to the given user.

2. The content recommendation system according to claim 1, characterized in that the content extraction means extracts some content from the content included in the list acquired by the list acquisition means on the basis of attributes of the content, and further extracts some content from some extracted content on the basis of the feature information of the some content and the user preference information acquired by the user preference information acquisition means.

3. The content recommendation system according to claim 1 or 2, characterized in that:

the list acquisition means acquires two or more lists, and

the content extraction means extracts some or all content from content included in each of the two or more lists acquired by the list acquisition means, and extracts some content based on the user preference information acquired by the user preference information acquisition means from the extracted content.

4. The content recommendation system according to claim 3, characterized in that the content extraction means extracts pieces of content of which the number corresponds to the attribute information of the given user from the content included in each of the two or more lists acquired by the list acquisition means, and extracts some content based on the user preference information acquired by the user preference information acquisition means from the extracted content.

5. The content recommendation system according to any one of claims 1 to 3, characterized in further comprising:

a list generation means for sequentially generating lists of content over time; and

a list storage means for storing the lists generated by the list generation means in association with a user attribute information range,

wherein the list acquisition means acquires the lists stored in the list storage means in association with the attribute information of the given user.

6. The content recommendation system according to claim 5, characterized in that the list generation means calculates a ratio between the number of times the content presentation means has presented each piece of the content to the user and the number of times the user corresponding to attribute information of each range has performed a specific manipulation for the content, and generates a list for each attribute information range on the basis of the ratio.

7. A content recommendation method characterized in comprising:

a user attribute information acquisition step of acquiring attribute information of a given user;

a list acquisition step of acquiring at least one list from among lists of content sequentially generated over time;

a user preference information acquisition step of acquiring user preference information, which is feature information of content preferred by the given user;

a content extraction step of extracting some content from content included in the list acquired in the list acquisition step on the basis of the user preference information acquired in the user preference information acquisition step; and

a content presentation step of presenting the content extracted in the content extraction step to the given user.

8. A content recommendation device characterized in comprising:

a user attribute information acquisition means for acquiring attribute information of a given user;

a list acquisition means for acquiring at least one list from among lists of content sequentially generated over time;

a user preference information acquisition means for acquiring user preference information, which is feature information of content preferred by the given user;

a content extraction means for extracting some content from content included in the list acquired by the list acquisition means on the basis of the user preference information acquired by the user preference information acquisition means; and

a content presentation means for presenting the content extracted by the content extraction means to the given user.

9. An information storage medium storing a program for causing a computer to function as:

a user attribute information acquisition means for acquiring attribute information of a given user;

a list acquisition means for acquiring at least one list from among lists of content sequentially generated over time;

a user preference information acquisition means for acquiring user preference information, which is feature information of content preferred by the given user;

a content extraction means for extracting some content from content included in the list acquired by the list acquisition means on the basis of the user preference information acquired by the user preference information acquisition means; and

a content presentation means for presenting the content extracted by the content extraction means to the given user.