An Improved Collaborative Filtering Model Based on Rough Set
Collaborative filtering has been proved to be one of the most successful techniques in recommender system. However, a rapid expansion of Internet and e-commerce system has resulted in many challenges. In order to alleviate sparsity problem and recommend more accurately, a collaborative filtering model based on rough set is proposed. The model uses rough set theory to fill vacant ratings firstly, then adopts rough user clustering algorithm to classify each user to lower or upper approximation based on similarity, and searches the target user’s nearest neighborhoods and make top-N recommendations at last. Well-designed experiments show that the proposed model has smaller MAE than traditional collaborative filtering and collaborative filtering based on user clustering, which indicates that the proposed model performs better, and can improve recommendation accuracy effectively.
KeywordsCollaborative Filtering Rough Set Lower or Upper Approximation
Unable to display preview. Download preview PDF.
- 1.Lu, L.Y., Medo, M., Yeung, C.H., Zhang, Y.C., Zhang, Z.K., Zhou, T.: Recommender system. Physics Reports Review Section of Physics Letters 519, 1–49 (2012)Google Scholar
- 4.Takacs, G., Pilaszy, I., Nemeth, B., Tikk, D.: Scalable Collaborative Filtering Approaches for Large Recommender System. Journal of Machine Learning Research 10, 623–656 (2009)Google Scholar
- 6.Ungar, L.H., Foster, D.P.: Clustering Methods for Collaborative Filtering. In: Proceedings of 1998 Workshop on Recommender Systems, pp. 114–129. AAAI (1998)Google Scholar
- 7.Li, T., Wang, J.D., Ye, F.Y., Feng, X.Y., Zhang, Y.D.: Collaborative Filtering Recommen-dation Algorithm Based on Clustering Basal Users. Systems Engineering and Electronics 29, 1178–1182 (2007)Google Scholar
- 8.Deng, A.I., Zuo, Z.Y., Zhu, Y.Y.: Collaborative Filtering Recommendation Algorithm Based on Item Clustering. Mini-Micro Systems 25, 1665–1670 (2004)Google Scholar
- 14.Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering. In: Hearst, M., Tong, R. (eds.) SIGIR 1999, pp. 230–237. ACM, New York (1999)Google Scholar
- 15.Zhang, Q.M., Shang, M.S., Zeng, W., Chen, Y., Lu, L.Y.: Empirical Comparison of Local Structural Similarity Indices for Collaborative-Filtering-Based Recommender Systems. In: Wang, B.H., Zhang, Y.C., Zhou, T., Castellano, C. (eds.) China-Europe 2010. Physics Procedia, vol. 3, pp. 1887–1896. Elsevier Science, Netherlands (2010)Google Scholar
- 17.Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Poland (1991)Google Scholar
- 18.Movielens Movie Rating Data Set, http://movielens.umn.edu/login