… By comparing similar items rather than similar customers, item-to-item collaborative filtering scales to very large data sets and produces high-quality recommendations. …internet, 7(1):76-80, 2003.
… We propose a fully decentralized collaborative filtering approach that is self … tested and compared our distributed collaborative filtering approach to centralized collaborative filtering and showed that it has similar performance …sigir 2005: 659-660
… Commerce is collaborative filtering which is based on the ratings of other customers who have similar preferences. However, collaborative filtering may not provide … for collaborative filtering. The algorithm uses the K-Means Clustering method …ecweb 2002: 254-261
… The increasing users and items restrict the development of collaborative filtering recommendation systems. Then a series of problems, such as sparsity, cold start and scalability, come out. In this paper, we add user preference based …wkdd 2009: 60-63
… The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the known preferences of multiple users to recommend items of interest …uai 2000: 473-480