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Method and System of Adaptive Tag Sharing based on Social Relationship Disclosure Number: IPCOM000239538D
Publication Date: 2014-Nov-14
Document File: 5 page(s) / 191K

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The Prior Art Database


The method and system in this disclosure is to provide the Adaptive Tag Sharing based on Social Relationship, and disclosures mechanism to build the Social Tags Model based on the analysis of Social Metadata from the user profile and Tags Metadata from the targeted article to populate the weight value for each tag.

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Method and System of Adaptive Tag Sharing based on Social Relationship

Internet users are often times overwhelmed by the flow of online information, hence the need for adequate systems that will help them manage such situations.

Recommender systems attempt to guide the user in a personalised way to interesting and useful articles in a large space of possible options by producing individualised recommendations as output. They are usually classified into two basic types according to how recommendations are produced : content-based recommendation, where a user is recommended articles similar to the ones that he preferred in the past; and collaborative recommendation (or collaborative filtering), where a user is recommended articles that people with similar tastes and preferences liked in the past. A recommender system requires a description of either the characteristics of the resources (for content-based recommendation), or of user preferences for resources in the form of evaluations or ratings (for collaborative recommendation).

Nowadays either poster or readers can post tag on given articles. The tag can be used for content recommendation. However, tag information is not tightly associated with social relationship. This caused one dilemma where either a lot of articles are recommended due to the characteristic of articles with assigned tags (topic centric), or a few articles are recommended because only a few relevant articles are tagged by direct readers/friend...