Research On The Key Issues Of Trust-based Personalized Recommender Systems In E-commerce

With the rapid popularization of Internet, virtual shopping environment of e-commerce not only provide new development chance for the enterprises, but also put forward how to handle the challenge of information overload of web products. As a means of personalized service, recommender systems can recommend interesting items for users, assist users to make decision, and become an good assistant for users' shopping online. Since the emergence of the first recommender system in the 1990s, remarkable progress in this field has been achieved, but there still exists some issues hard to overcome in traditional collaborative filtering technology. In the situation of the rapid development of web social networks, because of incorporating trust mechanism into traditional collaborative filtering recommender systems, trust-based recommender systems can improve or overcome these limitations effectively, and become one of the most important research projects.This thesis explored and researched some key issues of trust-based personalized recommender systems in e-Commerce. Firstly, it gave an overview of the theoretical research and development on personalized recommendation to date. The advantages of collaborative filtering technology and research challenges were analyzed. Secondly, from the definition and properties of network trust, the characteristic of the trust evaluation models were analyzed and compared. Thirdly, a general model of trust-based personalized recommender system and system framework was presented. Then the author gave the research work on trust-based personalized recommendation algorithm for user's multiple interests, diversity of recommendation list items and recommendation attack of trust-based recommender system.The innovational works of this thesis are as following:(1) Based on the basic modal of traditional collaborative filtering system and the characteristic of web social network, the author presented a general modal of trust-based e-commerce personalized recommender systems, which is inclusive, and can extend to many different recommendation algorithm. Meanwhile, the system framework of trust-based e-commerce personalized recommender system is discussed.(2) Finding that current profile-level trust modal is not suitable to items recommendation for user's multiple-interests, the authors analyzed the reason and present a novel collaborative filtering algorithm based on topic-level trust modal, which can deal with the issue of user's multiple-interests. A series of experiments show that the algorithm achieves highly on the accuracy and robustness under shilling attacks.(3) Considering the user's complete spectrum of interests, the limitation of research on recommender system only paying attention to improve the accuracy of recommendation algorithm is the neglect of recommendation diversification. This paper proposes a novel recommendation diversification algorithm for trust based E-commerce personalized recommender systems, which is designed to balance the accuracy and the diversification of the recommendation list. The experiment shows that the algorithm can improve the recommendation diversification.(4) Malicious attack modes because of trust propagation was discussed. According to the sparsity of rating matrix of trust neighors, the author presented a defending method by tracing malicious users with data lineage, and limiting the malicious users to distrust users.