A Constrained Spreading Activation Approach to Collaborative Filtering

  • Josephine Griffith
  • Colm O’Riordan
  • Humphrey Sorensen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4253)


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.


Recommender System Weighted Edge Threshold Function Collaborative Filter Link Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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)Google Scholar
  2. 2.
    Balabanovic, M., Shoham, Y.: Fab: Content-based, collaborative recommendation. Communications of the ACM 40(3), 66–72 (1997)CrossRefGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Cohen, P., Kjeldsen, R.: Information retrieval by constrained spreading activation on semantic networks. Information Processing and Management 23(4), 255–268 (1987)CrossRefGoogle Scholar
  6. 6.
    Crestani, F., Lee, P.L.: Searching the web by constrained spreading activation. Information Processing and Management 36, 585–605 (2000)CrossRefGoogle Scholar
  7. 7.
    Croft, W.B.: Combining approaches to information retrieval. In: Advances in Information Retrieval, pp. 1–36. Kluwer Academic Publishers, Dordrecht (2000)Google Scholar
  8. 8.
    Herlocker, J.L.: Understanding and Improving Automated Collaborative Filtering Systems. Phd thesis, University of Minnesota (2000)Google Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    Mirza, B., Keller, B., Ramakrishnan, N.: Studying recommendation algorithms by graph analysis. Journal of Intelligent Information Systems 20, 131–160 (2003)CrossRefGoogle Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    Schwartz, M.F., Wood, C.M.: Discovering shared interests using graph analysis. Communications of the ACM 36, 78–89 (1993)CrossRefGoogle Scholar
  17. 17.
    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)Google Scholar
  18. 18.
    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)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Josephine Griffith
    • 1
  • Colm O’Riordan
    • 1
  • Humphrey Sorensen
    • 2
  1. 1.Dept. of Information TechnologyNational University of IrelandGalwayIreland
  2. 2.Dept. of Computer ScienceUniversity College CorkCorkIreland

Personalised recommendations