Scalable collaborative filtering using cluster-based smoothing

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 approaches have been shown to suffer from two fundamental problems: data sparsity and difficulty in scalability. Alternatively, the model-based approaches have been proposed to alleviate these problems, but these approaches tends to limit the range of users. In this paper, we present a novel approach that combines the advantages of these two kinds of 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-the-art collaborative filtering algorithms. Categories and Subject Descr...

• Type: Conference
• Year: 2005
• Where: SIGIR
• Authors: Gui-Rong Xue, Chenxi Lin, Qiang Yang, Wensi Xi, Hua-Jun Zeng, Yong Yu, Zheng Chen
• Keywords: Collaborative Filtering, Memory-based Approaches, Model-based Approaches, SIGIR 2005