iTrustU: a blog recommender system based on multi-faceted trust and collaborative filtering

Blogs give users a channel to express their knowledge and feelings with individuals worldwide, explaining the exponential growth of new blogs. However, due to the diverse subjects covered by bloggers, bloggers/readers have difficulty in finding valuable articles from the hundreds of millions of blogs on the Internet. To help ease information overload in the blogosphere, this work proposes a trust-enhanced collaborative filtering approach that integrates multi-faceted trust based on article type and user similarity. An online blog article recommender system, called iTrustU, is also designed to evaluate the effectiveness of the proposed approach in terms of accuracy and quality of recommendations. Results of a 45-day online experiment with 179 participants from the Internet demonstrate that the proposed integrated approach yields a significantly higher accuracy than traditional approaches, especially for cold-start users. Analysis results indicate that trust and similarity among bloggers/readers have a significantly positive correlation in the blogosphere. Effective recommender systems can be achieved by exploiting trust relationships in a trust network. The proposed approach is applicable not only to the blogosphere, but also to online social communities when trust relationships already exist between users.