Abstract
In this paper, we describe a collaborative filtering approach that aims to use features of users and items to better represent the problem space and to provide better recommendations to users. The goal of the work is to show that a graph-based representation of the problem domain, and a constrained spreading activation approach to effect retrieval, has as good, or better, performance than a traditional collaborative filtering approach using Pearson Correlation. However, in addition, the representation and approach proposed can be easily extended to incorporate additional information.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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: Proceedings of the Fifth ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 1999), San Diego, CA, pp. 201–212 (1999)
Balabanovic, M., Shoham, Y.: Fab: Content-based, collaborative recommendation. Communications of the ACM 40(3), 66–72 (1997)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as classification: Using social and content-based information in recommendation. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI 1998), pp. 714–721. AAAI Press, Menlo Park (1998)
Calderon-Benavides, M.L., Gonzalez-Caro, C.N., Perez-Alcazar, J., Garcia-Diaz, J.C., Delgado, J.: A comparison of several predictive algorithms for collaborative filtering on multi-valued ratings. In: Proceedings of the 2004 ACM Symposium on Applied Computing, pp. 1033–1039 (2004)
Cohen, P., Kjeldsen, R.: Information retrieval by constrained spreading activation on semantic networks. Information Processing and Management 23(4), 255–268 (1987)
Crestani, F., Lee, P.L.: Searching the web by constrained spreading activation. Information Processing and Management 36, 585–605 (2000)
Croft, W.B.: Combining approaches to information retrieval. In: Advances in Information Retrieval, pp. 1–36. Kluwer Academic Publishers, Dordrecht (2000)
Herlocker, J.L.: Understanding and Improving Automated Collaborative Filtering Systems. Phd thesis, University of Minnesota (2000)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS) 22, 5–53 (2004)
Huang, Z., Chen, H., Zeng, D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems 22(1), 116–142 (2004)
Huang, Z., Chung, W., Chen, H.: A graph model for e-commerce recommender systems. Journal of the American Society for Information Science and Technology 55(3), 259–274 (2004)
McLaughlin, M.R., Herlocker, J.L.: A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In: Proceedings of the 27th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 329–336 (2004)
Mirza, B., Keller, B., Ramakrishnan, N.: Studying recommendation algorithms by graph analysis. Journal of Intelligent Information Systems 20, 131–160 (2003)
O’Riordan, C., Sorensen, H.: Multi-agent based collaborative filtering. In: Klusch, M., et al. (eds.) Cooperative Information Agents 1999. LNCS (LNAI). Springer, Heidelberg (1999)
Palau, J., Montaner, M., López, B., de la Rosa, J.L.: Collaboration analysis in recommender systems using social networks. In: Klusch, M., Ossowski, S., Kashyap, V., Unland, R. (eds.) CIA 2004. LNCS, vol. 3191, pp. 137–151. Springer, Heidelberg (2004)
Schwartz, M.F., Wood, C.M.: Discovering shared interests using graph analysis. Communications of the ACM 36, 78–89 (1993)
Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating word of mouth. In: Proceedings of the Annual ACM SIGCHI on Human Factors in Computing Systems (CHI 1995), pp. 210–217 (1995)
Xue, G.-R., Huang, S., Yu, Y., Zeng, H.-J., Chen, Z., Ma, W.-Y.: Optimizing web search using spreading activation on the clickthrough data. In: Zhou, X., Su, S., Papazoglou, M.P., Orlowska, M.E., Jeffery, K. (eds.) WISE 2004. LNCS, vol. 3306, pp. 409–414. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Griffith, J., O’Riordan, C., Sorensen, H. (2006). A Constrained Spreading Activation Approach to Collaborative Filtering. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_97
Download citation
DOI: https://doi.org/10.1007/11893011_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46542-3
Online ISBN: 978-3-540-46544-7
eBook Packages: Computer ScienceComputer Science (R0)