Content recommendation apparatus and method using tag cloud

• US 8,209,337 B2
• Filed: 11/19/2008
• Issued: 06/26/2012
• Est. Priority Date: 11/19/2007
• Status: Expired due to Fees

• Current Assignee: Seoul National University Industry Foundation (Seoul National University), Synergy Innovation Co., Ltd.
• Original Assignee: Seoul National University Industry Foundation (Seoul National University), Synergy Innovation Co., Ltd.
• Inventors: Park, Jong-Hun
• Primary Examiner(s): Channavajjala, Srirama
• Application Number:
  US12/273,710
• Publication Number:
  US 20090132526A1
• Time in Patent Office: 1,315 Days
• Field of Search: 707/719, 707726-729, 707731-732, 707747-751, 707/758, 707/776, 707/785, 707/920, 707/688, 707705-711, 707/727, 705 26- 27, 705 729- 734, 706 45- 50, 340/572.1, 340/568.5, 702/179
• US Class Current: 707/749
• CPC Class Codes: G06F 16/38 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually

Abstract

Content recommendation apparatus and methods using a tag cloud provide a content recommendation service via a network. The apparatus includes a content tag cloud generating module configured to generate a content tag cloud by analyzing a tag assigned to each content and accumulating frequencies per tag of each content. The apparatus also includes a user tag cloud generating module configured to generate a user tag cloud by accumulating frequencies per tag of contents used by a user. The apparatus further includes a similarity computing module and a recommending module. The similarity computing module is configured to compute a similarity between users using the user tag cloud, and the recommending module is configured to recommend content by computing a probability that a target user will use a specific content based on the computed similarity between users.

Claims

1. Content recommendation apparatus, the apparatus comprising:

a content recommendation server providing a content recommendation service and recommending content via a network; and

a plurality of user terminals receiving the content provided by the content recommendation server via the network,

wherein the content recommendation server comprises

a content tag cloud generating module including a processor to generate a content tag cloud by analyzing a tag assigned to each content and accumulating frequencies per tag of each content, wherein a plurality of tags as to one content are created by a plurality of users and accumulated as the content tag cloud as to the one content and are associated with words related to the content;

a user tag cloud generating module including a processor to generate a user tag cloud by accumulating frequencies per tag of contents used by a user;

a similarity computing module including a processor to compute a similarity between users using the user tag cloud; and

a recommending module including a processor to recommend a content by computing a probability that a target user will use a specific content based on the computed similarity between users,

wherein, in the accumulation of frequencies per tag of contents used by a user, the user tag cloud generating module normalizes a frequency per tag of a content used by the user and accumulates the normalized frequency per tag,

wherein the recommending module determines a user neighborhood similar to a target user and recommends a content based on a similarity between the target user and a user in the determined user neighborhood, and

wherein the recommending module determines a user neighborhood based on users who have used at least two contents in a set of contents used by the target user and determines N users from the user neighborhood most similar to the target user based on the similarity between users computed by the similarity computing module.

2. The content recommendation apparatus of claim 1 wherein the similarity computing module computes a cosine value of an angle between two vectors of tag clouds of two users.

3. The content recommendation apparatus of claim 1 further comprising a second recommending module configured to recommend a package of items or an online shop having a large amount of content recommended by the recommending module.

4. A content recommendation method of a content recommendation apparatus, the apparatus comprising:

a content recommendation server providing a content recommendation service and recommending content via a network; and

a plurality of user terminals receiving the content provided by the content recommendation server via the network, the method comprising;

analyzing a tag assigned to each content and accumulating frequencies per tag of each content to generate a content tag cloud by a processor included in a content cloud generating module of the content recommendation server, wherein a plurality of tags as to one content are created by a plurality of users and accumulated as the content tag cloud as to the one content and are associated with words related to the content;

accumulating frequencies per tag of contents used by a user to generate a user tag cloud by a processor included in a user tag cloud generating module of the content recommendation server;

computing a similarity between users using the user tag cloud by a processor included in a similarity computing module of the content recommendation server; and

computing a probability that a target user will use a specific content based on the computed similarity between users to recommend a content by a processor included in a recommendation module of the content recommendation server,

wherein, in accumulation of frequencies per tag of contents used by a user, generating the user tag cloud normalizes a frequency per tag of a content used by the user and accumulates the normalized frequency per tag,

wherein the recommending a content comprises;

determining a user neighborhood similar to a target user; and

recommending a content based on a similarity between the target user and a user in the determined user neighborhood,

wherein a user neighborhood is determined based on users who have used at least two contents in a set of contents used by the target user, and

wherein N users are determined from the user neighborhood most similar to the target user based on the similarity between users computed by the similarity computing module.

5. The content recommendation method of claim 4 wherein computing the similarity comprises computing a cosine value of an angle between two vectors of tag clouds of two users as similarity.

6. The content recommendation method of claim 4, further comprising:

recommending a package of items or an online shop having a large amount of content recommended in the recommending step.

7. A computer readable storage medium having a computer program stored thereon for implementing a content recommendation function using a tag cloud in a computer with a processor, the function comprising:

generating a content tag cloud by analyzing a tag assigned to each content and accumulating frequencies per tag of each content to generate a content tag cloud by a processor included in a content cloud generating module of the content recommendation server, wherein a plurality of tags as to one content are created by a plurality of users and accumulated as the content tag cloud as to the one content and are associated with words related to the content

generating a user tag cloud by accumulating frequencies per tag of contents used by a user to generate a user tag cloud by a processor included in a user tag cloud generating module of the content recommendation server;

computing a similarity between users using the user tag cloud by a processor included in a similarity computing module of the content recommendation server;

computing a similarity between users using the user tag cloud by a processor included in a similarity computing module of the content recommendation server;

computing a probability that a target user will use a specific content based on the computed similarity between users to recommend a content by a processor included in a recommendation module of the content recommendation server,

wherein, in accumulation of frequencies per tag of contents used by a user, generating the user tag cloud normalizes a frequency per tag of a content used by the user and accumulates the normalized frequency per tag,

wherein the recommending a content comprises;

determining a user neighborhood similar to a target user; and

recommending a content based on a similarity between the target user and a user in the determined user neighborhood

determining a user neighborhood based on users who have used at least two contents in a set of contents used by the target user; and

determining N users from the user neighborhood most similar to the target user based on the similarity between users computed by the similarity computing module.