Abstract
With the development of electronic commerce, a lot of recommendation techniques has been developed. Collaborative filtering(CF) is one of the most important technologies. However, traditional collaborative filtering suffers sparsity and scalability problems, which results in poor quality of prediction in recommendation systems. To solve these problems, this paper proposed a recommendation algorithm combining clustering method and slope one scheme. This approach uses clustering algorithms to partition the set of items to several clusters based on user rating data, and then we use slope one scheme to predict ratings independently for unknown items based on which cluster the items belong to. We make experiments on the standard benchmark Movielens data sets and compare our approach with the basic slope one scheme. The results show that our algorithm outperforms the slope one scheme.
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References
Breese, J., Heckerman, D., Kadie, C., et al.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
Chien, Y., George, E.: A Bayesian Model for Collaborative Filtering. In: Proceedings of the 7th International Workshop on Artificial Intelligence and Statistics (1999)
Hofmann, T.: Latent Semantic Models for Collaborative Filtering. ACM Transactions on Information Systems (TOIS) 22(1), 89–115 (2004)
Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs (1988)
Lemire, D., Maclachlan, A.: Slope One Predictors for Online Rating-based Collaborative Filtering. Society for Industrial Mathematics (2005)
Linden, G., Smith, B., York, J.: Amazon. com Recommendations: Item-to-item Collaborative Filtering. IEEE Internet Computing 7(1), 76–80 (2003)
Lloyd, S.: Least Squares Quantization in PCM. IEEE Transactions on Information Theory 28(2), 129–137 (2002)
MacKay, D.: An Example Inference Task: Clustering. Inference and Learning Algorithms on Information Theory, ch. 20, pp. 284–292
Quan, T., Fuyuki, I., Shinichi, H.: Improving Accuracy of Recommender System by Clustering Items Based on Stability of User Similarity. In: Proceedings of International Conference on IAWTIC, p. 61. IEEE Computer Society, Washington, DC (2007)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175–186. ACM, New York (1994)
Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: Proceedings of the 10th International Conference on World Wide Web(WWW), pp. 285–295. ACM, New York (2001)
Shani, G., Heckerman, D., Brafman, R.: An MDP-based Recommender System. Journal of Machine Learning Research 6(2), 1265 (2006)
Su, X., Khoshgoftaar, T.: A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence 2 (2009)
Ungar, L., Foster, D.: Clustering Methods for Collaborative Filtering. In: AAAI Workshop on Recommendation Systems, pp. 112–125 (1998)
Wang, P., Ye, H.: A Personalized Recommendation Algorithm Combining Slope One Scheme and User Based Collaborative Filtering. In: Proceedings of IEEE International Conference on Industrial and Information Systems(IIS 2009), pp. 152–154. IEEE, Los Alamitos (2009)
Zhang, D.: An Item-based Collaborative Filtering Recommendation Algorithm Using Slope One Scheme Smoothing. In: Proceedings of IEEE Second International Symposium on Electronic Commerce and Security(ISECS 2009), vol. 2, pp. 215–217. IEEE, Los Alamitos (2009)
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Mi, Z., Xu, C. (2012). A Recommendation Algorithm Combining Clustering Method and Slope One Scheme. In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_23
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DOI: https://doi.org/10.1007/978-3-642-24553-4_23
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