The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Similarity measure and instance selection for collaborative filtering
WWW '03 Proceedings of the 12th international conference on World Wide Web
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Authority and ranking effects in data fusion
Journal of the American Society for Information Science and Technology
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Collaborative filtering has been very successful in both applications and researches. In real situation, different users may have different influences on other users' decisions. Those authoritative users usually play more important roles. But few existing collaborative filtering algorithms consider the authorities of users. In this paper, we present the concepts of global and domain authorities of users, and apply them in collaborative filtering algorithms. This paper designs the experiments and discusses the effects of global and domain authorities. The initial results show our method can improve the performance of collaborative filtering algorithm.