The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Scalable collaborative filtering using cluster-based smoothing
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.
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Cites
Item-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
Evaluating collaborative filtering recommender systems
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004) · ACM Transactions on Information Systems · doi:10.1145/963770.963772 · Cited by 5,798
GroupLens: an open architecture for collaborative filtering of netnews
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994) · Proceedings of the 1994 ACM conference on Computer supported cooperative work - CSCW '94 · doi:10.1145/192844.192905 · Cited by 5,058
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
Breese, J. S., Heckerman, D. E., & Kadie, C. M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.7363 · Cited by 4,772
Fab: content-based, collaborative recommendation
Balabanović, M., & Shoham, Y. (1997) · Communications of the ACM · doi:10.1145/245108.245124 · Cited by 2,934
(1992) · doi:10.5555/176313.176316 · Cited by 2,892
An Algorithmic Framework for Performing Collaborative Filtering
Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130372 · Cited by 2,512
GroupLens: applying collaborative filtering to Usenet news
Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997) · Communications of the ACM · doi:10.1145/245108.245126 · Cited by 2,367
An algorithmic framework for performing collaborative filtering
Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999) · Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/312624.312682 · Cited by 2,011
Proceedings of the 20th conference on Uncertainty in artificial intelligence
Meek, C., Chickering, M., & Halpern, J. Y. (2004) · Cited by 1,574
Cited by
Circle-based recommendation in online social networks
Yang, X., Steck, H., & Liu, Y. (2012) · Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2339530.2339728 · Cited by 346
Metadata
- Authors
- Xue, G.-R., Lin, C., Yang, Q., Xi, W., Zeng, H.-J., Yu, Y., & Chen, Z.
- Venue
- Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2005-08-15
- Pages
- 114-121
- OpenAlex
- W2113273124
- Cited by
- 658
- References
- 20
- FWCI
- 20.07
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Xue, G.-R., Lin, C., Yang, Q., Xi, W., Zeng, H.-J., Yu, Y., & Chen, Z. (2005). Scalable collaborative filtering using cluster-based smoothing. Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/1076034.1076056
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
