Advertisement

Retrieval of Collaborative Filtering Nearest Neighbors in a Content-Addressable Space

  • Shlomo Berkovsky
  • Yaniv Eytani
  • Larry Manevitz
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 3)

Abstract

Collaborative Filtering (CF) is considered one of the popular and most widely used recommendation techniques. It is aimed at generating personalized item recommendations for the users based on the assumption that similar users have similar preferences and like similar items. One of the major drawbacks of the CF is its limited scalability, as the CF computational effort increases linearly with the number of users and items. This work presents a novel variant of the CF, employed over a content-addressable space. This heuristically decreases the computational effort required by the CF by restricting the nearest neighbors search applied by the CF to a set potentially highly similar users. Experimental evaluation demonstrates that the proposed approach is capable of generating accurate recommendations, while significantly improving the performance in comparison with the traditional implementation of the CF.

Keywords

Active User Recommender System Collaborative Filter Average Similarity Mean Square Difference 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aguzzoli, S., Avesani, P., Massa, P.: Collaborative Case-Based Recommender System. In: Proceedings of the ECCBR Conference (1997)Google Scholar
  2. 2.
    Bogaerts, S., Leake, D.: Facilitating CBR for Incompletely-Described Cases: Distance Metrics for Partial Problem Descriptions. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, Springer, Heidelberg (2004)Google Scholar
  3. 3.
    Breese, J., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the UAI Conference (1998)Google Scholar
  4. 4.
    Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)CrossRefGoogle Scholar
  5. 5.
    Chee, S.H.S., Han, J., Wang, K.: RecTree: An Efficient Collaborative Filtering Method. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, Springer, Heidelberg (2001)Google Scholar
  6. 6.
    Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A Constant Time Collaborative Filtering Algorithm. Information Retrieval Journal 4(2) (2001)Google Scholar
  7. 7.
    Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., Sarwar, B., Herlocker, J., Riedl, J.: Combining Collaborative Filtering with Personal Agents for Better Recommendations. In: Proceedings of the AAAI Conference (1999)Google Scholar
  8. 8.
    Han, P., Xie, B., Yang, F., Shen, R.: A Scalable P2P Recommender System Based on Distributed Collaborative Filtering. Expert Systems with Applications Journal 27(2) (2004)Google Scholar
  9. 9.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering. In: Proceedings of the SIGIR Conference (1999)Google Scholar
  10. 10.
    McJones, P.: Eachmovie Collaborative Filtering Data Set (1997), http://research.compaq.com/SRC/eachmovie/
  11. 11.
    Miller, B.N., Konstan, J.A., Riedl, J.: PocketLens: Toward a Personal Recommender System. ACM Transactions on Information Systems 22(3) (2004)Google Scholar
  12. 12.
    Morita, M., Shinoda, Y.: Information Filtering Based on User Behavior Analysis and Best Match Retrieval. In: Proceedings of the SIGIR Conference (1994)Google Scholar
  13. 13.
    Pennock, D.M., Horvitz, E., Giles, C.L.: Social Choice Theory and Recommender Systems: Analysis of the Axiomatic Foundations of Collaborative Filtering. In: Proceedings of the AAAI Conference (2000)Google Scholar
  14. 14.
    Plaxton, C., Rajaraman, R., Richa, A.: Accessing Nearby Copies of Replicated Objects in a Distributed Environment. In: Proceedings of the ACM SPAA Conference (1997)Google Scholar
  15. 15.
    Ratnasamy, S., Francis, P., Handley, M., Karp, R., Shenker, S.: A Scalable Content-Addressable Network. In: Proceedings of the SIGCOMM Conference (2001)Google Scholar
  16. 16.
    Resnick, P., Varian, H.R.: Recommender Systems. Communications of the ACM 40(3) (1997)Google Scholar
  17. 17.
    Ricci, F., Venturini, A., Cavada, D., Mirzadeh, N., Blaas, D., Nones, M.: Product Recommendation with Interactive Query Management and Twofold Similarity. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, Springer, Heidelberg (2003)CrossRefGoogle Scholar
  18. 18.
    Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill Pubslishers, New York (1983)Google Scholar
  19. 19.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of Recommendation Algorithms for E-Commerce. In: Proceedings of the EC Conference (2000)Google Scholar
  20. 20.
    Sarwar, B.M., Konstan, J.A., Riedl, J.: Distributed Recommender Systems: New Opportunities for Internet Commerce. In: Internet Commerce and Software Agents: Cases, Technologies and Opportunities, Idea Group Publishers, USA (2001)Google Scholar
  21. 21.
    Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating “Word of Mouth”. In: Proceedings of the CHI Conference (1995)Google Scholar
  22. 22.
    Tveit, A.: Peer-to-Peer Based Recommendations for Mobile Commerce. In: Proceedings of the WMC Workshop (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Shlomo Berkovsky
    • 1
  • Yaniv Eytani
    • 1
  • Larry Manevitz
    • 1
  1. 1.Computer Science DepartmentUniversity of HaifaHaifaIsrael

Personalised recommendations