Learning to recommend.

• Other Authors: Ma, Hao.
• Format: Others
• Language: English; Chinese
• Published: 2010
• Subjects: Filters (Mathematics); Information storage and retrieval systems > Social aspects; Recommender systems (Information filtering)
• Online Access: http://library.cuhk.edu.hk/record=b6074849; http://repository.lib.cuhk.edu.hk/en/item/cuhk-344482

As one of the social relations, "distrust" also performs an important role in online Web sites. We also observe that distrust information can also be incorporated to improve recommendation quality. Hence, the last part of this thesis studies the problem on how to improve recommender system by considering explicit distrust information among users. We make the assumption that users' distrust relations can be interpreted as the "dissimilar" relations since user ui distrusts user ud indicates that user ui disagrees with most of the opinions issued by user ud. Based on this intuition, the distrust relations between users can be easily modeled by adding the regularization term into the objective functions of the user-item matrix factorization. The experiments on the Epinions dataset indicate that distrust information is at least as important as trust information. === However, the data sparsity problem of the involved user-item matrix seriously affects the recommendation quality. Many existing approaches to recommender systems cannot easily deal with users who have made very few ratings. The objective of this thesis is to study how to build effective and efficient approaches to improve the recommendation performance. === In this thesis, we first propose two collaborative filtering methods which only utilize the user-item matrix for recommendations. The first method is a neighborhood-based collaborative filtering method which designs an effective missing data prediction algorithm to improve recommendation quality, while the second one is a model-based collaborative filtering method which employs matrix factorization technique to make the recommendation more accurate. === In view of the exponential growth of information generated by online users, social contextual information analysis is becoming important for many Web applications. Hence, based on the assumption that users can be easily influenced by the friends they trust and prefer their friends' recommendations, we propose two recommendation algorithms by incorporating users' social trust information. These two methods are based on probabilistic matrix factorization. The complexity analysis indicates that our approaches can be applied to very large datasets since they scale linearly with the number of observations, while the experimental results show that our methods perform better than the state-of-the-art approaches. === Recommender Systems are becoming increasingly indispensable nowadays since they focus on solving the information overload problem, by providing users with more proactive and personalized information services. Typically, recommender systems are based on Collaborative Filtering, which is a technique that automatically predicts the interest of an active user by collecting rating information from other similar users or items. Due to their potential commercial values and the associated great research challenges, Recommender systems have been extensively studied by both academia and industry recently. === Ma, Hao. === "December 2009." === Advisers: Irwin King; Michael R. Lyu. === Source: Dissertation Abstracts International, Volume: 72-01, Section: B, page: . === Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. === Includes bibliographical references (leaves 136-154). === Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. === Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. === Abstract also in Chinese.