Abstract
Collaborative filtering and content-based filtering are two main approaches to make recommendations in recommender systems. While each approach has its own strengths and weaknesses, combining the two approaches can improve recommendation accuracy. In this paper, we present a graph-based method that allows combining content information and rating information in a natural way. The proposed method uses user ratings and content descriptions to infer user-content links, and then provides recommendations by exploiting these new links in combination with user-item links. We present experimental results showing that the proposed method performs better than a pure collaborative filtering, a pure content-based filtering, and a hybrid method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. Knowl. and Data Eng. 17(6) (2005)
Aggarwal, C.C., Wolf, J.L., Wu, K.L., Yu, P.S.: Horting Hatches an Egg: A New Graph-Theoretic Approach to Collaborative Filtering. In: Proc. of ACM SIGKDD 1999 (1999)
Balabanovic, M., Shoham, Y.: FAB: Content-based, collaborative recommendation. Communication of the ACM 40(3), 66–72 (1997)
Balisico, J., Hofman, T.: Unifying collaborative and content-based filtering. In: Proceedings. of Int. Conf. on Machine learning (ICML 2004) (2004)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as classification: Using social and content-based information in recommendation. In: Proc. Nat. Conf. on AI, pp. 714–720 (1998)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proc. of 14th Conf. on Uncertainty in AI, pp. 43–52 (1998)
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining contentbased and collaborative fillters in an online newspaper. In: Proc. of ACM SIGIR Workshop on Recommender Systems (1999)
Crammer, K., Singer, Y.: Pranking with ranking. In: Advances in Neural Information Processing Systems, vol. 14, pp. 641–647 (2002)
Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR 1999: Proc. of the 22nd Inter. Conf. on Research and Development in Information Retrieval (SIGIR), pp. 230–237 (1999)
Herlocker, J., Konstan, K., Terveen, L.G., Riedl, J.: Evaluating Collaborative Filtering Recommender Systems. ACM Trans. on Inform. Syst. 22(1), 5–53 (2004)
Huang, Z., Chung, W., Ong, T.-H., Chen, H.: A graph-based recommender system for digital library. In: Proc. of 2nd ACM/IEEE-CS Joint Conf. on Digital Libraries, pp. 65–73 (2002)
Huang, Z., Chen, H., Zeng, D.: Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering. ACM Trans. Inf. Syst. 22(1), 116–142 (2004)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Proc. of 18th Nat. Conf. on AI, pp. 187–192 (2002)
Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proc. of the 5th ACM Conference on Digital Libraries, pp. 195–204 (2000)
Phuong, N.D., Phuong, T.M.: Collaborative filtering by multitask learning. In: Proc. IEEE Int. Conf. on Research, Innovation and Vision for the Future, pp. 227–232 (2008)
Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review 13(5-6), 393–408 (1999)
Popescul, A., Ungar, L.H., Pennock, D.M., Lawrence, S.: Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments. In: Proc. 17th Conf. Uncertainty in Artificial Intelligence (2001)
Resnick, P., Varian, H.R.: Recommender systems. Special issue of Communications of the ACM, 56–58 (1997)
Shardanand, U., Maes, P.: Social information filltering: Algorithms for automating word of mouth. In: Human Factors in Computing Systems ACM CHI, pp. 210–217 (1995)
Wang, J., de Vries, A.P., Reinders, M.J.T.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proc. of SIGIR 2006, Seatle, USA (2006)
Yu, K., Schwaighofer, A., Tresp, V., Ma, W.-Y., Zhang, H.: Collaborative ensemble learning: Combining collaborative and content-based information filtering via hierarchical bayes. In: Proc. of the 19th Conference on Uncertainty in Artificial Intelligence, UAI (2003)
Weston, J., Elisseeff, A., Zhou, D., Leslie, C.S., Noble, W.S.: Protein ranking: From local to global structure in the protein similarity network. Proc. of National Academy of Science 101(17), 6559–6563 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Phuong, N.D., Thang, L.Q., Phuong, T.M. (2008). A Graph-Based Method for Combining Collaborative and Content-Based Filtering. In: Ho, TB., Zhou, ZH. (eds) PRICAI 2008: Trends in Artificial Intelligence. PRICAI 2008. Lecture Notes in Computer Science(), vol 5351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89197-0_80
Download citation
DOI: https://doi.org/10.1007/978-3-540-89197-0_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89196-3
Online ISBN: 978-3-540-89197-0
eBook Packages: Computer ScienceComputer Science (R0)