| Summary: | 碩士 === 國立交通大學 === 資訊管理研究所 === 99 === With the new generation of web-based communities, Web2.0, everyone can make a comment or share information via a blog website. However, with the rapid growth of blog articles that are produced every day, people are facing the problem of information overload. The Social bookmarking website provides users functions to post and push (recommend) articles and shows article push counts to help solve this problem, but it still cannot recommend articles to users based their personal interests. Accordingly, providing users with blog articles that suit their interests is an important issue. Existing researches on trust-network based recommendations mainly use explicitly specified relationship trust or users’ past rating data to predict trustworthiness between users and make recommendations. Very little research addresses the issues of trust propagations and recommendations based on the hybrid of trustworthiness derived from users’ past push behaviors and friend-interest relations.
In this work, we propose a novel hybrid trust network, which is constructed by combining two kinds of trust link. Push-following trust links are derived based on users’ post/push-behaviors. Friend-Similarity trust links are derived from the friend relations with the consideration of user preference similarity. In addition, we also derive user reputations by considering the number of following push after users’ post/push. A novel recommendation method, which combines the hybrid trust network and user reputations with user-based collaborative filtering, is proposed to recommend desirable articles satisfying personal interests. Our proposed method uses a dataset collected from the social bookmarking website funP to derive the push-following trustworthiness and friend interest relations. The experiment results demonstrate that our proposed method performs better than conventional approaches in recommending bog articles.
|
|---|
