Scalable collaborative filtering using cluster-based smoothing

Authors:
Gui-Rong Xue;Chenxi Lin;Qiang Yang;WenSi Xi;Hua-Jun Zeng;Yong Yu;Zheng Chen
Affiliations:
Shanghai Jiao-Tong University, Shanghai, P.R. China;Shanghai Jiao-Tong University, Shanghai, P.R. China;Hong Kong University of Science and Technology, Clearwater Bay, Kowloon, Hong Kong;Virginia Polytechnic Institute and State University, Virginia;Microsoft Research Asia, Beijing, P.R. China;Shanghai Jiao-Tong University, Shanghai, P.R. China;Microsoft Research Asia, Beijing, P.R. China
Venue:
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2005

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Abstract

Memory-based approaches for collaborative filtering identify the similarity between two users by comparing their ratings on a set of items. In the past, the memory-based approach has been shown to suffer from two fundamental problems: data sparsity and difficulty in scalability. Alternatively, the model-based approach has been proposed to alleviate these problems, but this approach tends to limit the range of users. In this paper, we present a novel approach that combines the advantages of these two approaches by introducing a smoothing-based method. In our approach, clusters generated from the training data provide the basis for data smoothing and neighborhood selection. As a result, we provide higher accuracy as well as increased efficiency in recommendations. Empirical studies on two datasets (EachMovie and MovieLens) show that our new proposed approach consistently outperforms other state-of-art collaborative filtering algorithms.