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Scalable collaborative filtering using cluster-based smoothing (2005)
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| Venue: | In Proc. of SIGIR |
| Citations: | 114 - 7 self |
BibTeX
@INPROCEEDINGS{Xue05scalablecollaborative,
author = {Gui-rong Xue and Chenxi Lin and Qiang Yang and Wensi Xi and Hua-jun Zeng and Yong Yu and Zheng Chen},
title = {Scalable collaborative filtering using cluster-based smoothing},
booktitle = {In Proc. of SIGIR},
year = {2005},
pages = {114--121}
}
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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 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.
