An Improved Collaborative Filtering Method through Content Association

(E-pub Ahead of Print)

Author(s): Runyuan Sun*, Ke Ji, Kun Ma, Dongfeng Yuan.

Journal Name: Recent Patents on Computer Science

Become EABM
Become Reviewer


Background: Collaborative recommendation systems make predictions by information filtering on the ratings of users on items. For data of a large volume and arriving dynamically, the traditional approach of collaborative filtering (CF) based on user-item rating matrix directly is not only expensive in terms of storage space for maintaining the matrix and time for training the prediction model, but also in terms of accuracy in predications for the items. Objective: The study aimed to overcome these deficiencies by associating content-based information (user’s tags and item’s keywords) through categories. Method: We propose a novel CF method based on the Tag-Category-Keyword three-level transitive rating model that uses item’s category as a bridge for computing similarity between user’s tags and item’s keywords and recommends the items with high similarity in content to users. Results: Experimental results on real dataset demonstrate the above improvements of our approach over the traditional CF methods. Conclusion: By associating the content information of both items and users, our method improves both efficiency and accuracy.

Keywords: Collaborative Filtering; Recommender Systems; User-item Rating Matrix; Tag-Keyword; Content Association

Rights & PermissionsPrintExport Cite as

Article Details

(E-pub Ahead of Print)
DOI: 10.2174/2213275912666181120141947
Price: $95