Collaborative Filtering Recommender Systems

  • J. Ben Schafer
  • Dan Frankowski
  • Jon Herlocker
  • Shilad Sen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4321)

Abstract

One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Aggarwal, C.C., Wolf, J., 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 International Conference on Knowledge discovery and data mining, San Diego, California, pp. 201–212. ACM, New York (1999)CrossRefGoogle Scholar
  3. 3.
    Avery, C., Resnick, P., Zeckhauser, R.: The Market for Evaluations. American Economic Review 89(3), 564–584 (1999)CrossRefGoogle Scholar
  4. 4.
    Balabanovíc, M., Shoham, Y.: Fab: Content-Based, Collaborative Recommendation. Communications of the ACM 40(3), 66–72 (1997)CrossRefGoogle Scholar
  5. 5.
    Basu, C., Hirsh, H., Cohen, W.W.: Recommendation. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence, Madison, Wisconsin, pp. 714–720. AAAI Press, Menlo Park (1998)Google Scholar
  6. 6.
    BBC Online News: Sony Admits Using Fake Reviewer, June 4 (2001), http://news.bbc.co.uk/1/hi/entertainment/film/1368666.stm
  7. 7.
    Berry, M.W., Dumais, S.T., O’Brian, G.W.: Using Linear Algebra for Intelligent Information Retrieval. Siam Review 37(4), 573–595 (1995)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Billsus, D., Pazzani, M.J.: Learning Collaborative Information Filters. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence. AAAI-98, Menlo Park, CA, pp. 46–94. Morgan Kaufmann, San Francisco (1998)Google Scholar
  9. 9.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceeding of the Fourteenth Conference on Uncertainty in Artificial Intelligence. UAI, Madison, Wisconsin, pp. 43–52. Morgan Kaufmann, San Francisco (1998)Google Scholar
  10. 10.
    Burke, R.: Hybrid Web Recomender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007)Google Scholar
  11. 11.
    Canny, J.: Collaborative Filtering with Privacy via Factor Analysis. In: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, Tampere, Finland, pp. 238–245. ACM Press, New York (2002)CrossRefGoogle Scholar
  12. 12.
    Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining Content-Based and Collaborative Filters in an Online Newspaper. In: Proceedings of the ACM SIGIR ’99 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley. ACM Press, New York (1999)Google Scholar
  13. 13.
    Condliff, M.K., Lewis, D., Madigan, D., Posse, C.: Bayesian Mixed-Effect Models for Recommender Systems. In: Proceedings of the SIGIR-99 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, California (1999) Google Scholar
  14. 14.
    Cosley, D., Lam, S.K., Albert, I., Konstan, J.A., Riedl, J.: Is Seeing Believing?: How Recommender System Interfaces Affect Users’ Opinions. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 585–592. ACM Press, Ft. Lauderdale (2003)Google Scholar
  15. 15.
    Dahlen, B.J., Konstan, J.A., Herlocker, J., Riedl, J.: Jump-starting Movielens: User Benefits Of Starting A Collaborative Filtering System With “Dead Data”. TR 98-017, University of MinnesotaGoogle Scholar
  16. 16.
    Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science 41(6), 159–168 (1998)Google Scholar
  17. 17.
    Delgado, J., Ishii, N.: Memory-Based Weighted Majority Prediction for Recommender Systems. In: 1999 SIGIR Workshop on Recommender Systems, pp. 1–5. University of California, Berkeley (1999)Google Scholar
  18. 18.
    Frankowski, D., Cosley, D., Sen, S., Terveen, L., Riedl, J.: You Are What You Say: Privacy Risks Of Public Mentions. In: Proceedings of SIGIR 2006, pp. 562–572 (2006)Google Scholar
  19. 19.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using Collaborative Filtering To Weave An Information Tapestry. Communications of the ACM 35(12), 61–70Google Scholar
  20. 20.
    Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A Constant-Time Collaborative Filtering Algorithm. Information Retrieval 4(2), 133–151 (2001)MATHCrossRefGoogle Scholar
  21. 21.
    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 Sixteenth National Conference on Artificial Intelligence, Orlando, pp. 439–446. AAAI Press, Menlo Park (1999)Google Scholar
  22. 22.
    Harper, F., Li, X., Chen, Y., Konstan, J.: An Economic Model Of User Rating In An Online Recommender System. In: Harper, F., Li, X., Chen, Y., Konstan, J. (eds.) Proceedings of the 10th International Conference on User Modeling, Edinburgh, UK, pp. 307–216 (2005)Google Scholar
  23. 23.
    Heckerman, D., Chickering, D.M., Meek, C., Rounthwaite, R., Kadie, C.: Dependency Networks for Inference, Collaborative Filtering, and Data Visualization. Journal of Machine Learning Research, 49-75 (2001)Google Scholar
  24. 24.
    Herlocker, J., Konstan, J.A., Terveen, L.G., Reidl, J.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)CrossRefGoogle Scholar
  25. 25.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An Algorithmic Framework For Performing Collaborative Filtering. In: Proceedings of the 22nd International Conference on Research and Development in Information Retrieval. SIGIR ’99, Berkeley, pp. 230–237. ACM Press, New York (1999)CrossRefGoogle Scholar
  26. 26.
    Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining Collaborative Filtering Recommendations. In: Herlocker, J.L., Konstan, J.A., Riedl, J. (eds.) Proceedings of the 2000 ACM conference on Computer supported cooperative work, Philadelphia, pp. 241–250. ACM Press, New York (2000)CrossRefGoogle Scholar
  27. 27.
    Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and Evaluating Choices in a Virtual Community of Use. In: Proceedings of ACM CHI’95 Conference on Human Factors in Computing Systems, Denver, pp. 194–201. ACM Press, New York (1995)Google Scholar
  28. 28.
    Hill, W.C., Hollan, J.D., Wroblewski, D., McCandless, T.: Edit Wear and Read Wear. In: Proceedings of the SIGCHI conference on Human factors in Computing Systems, Monterey, pp. 3–9. ACM Press, New York (1992)Google Scholar
  29. 29.
    Hofmann, T.: Latent Semantic Models For Collaborative Filtering. ACM Transactions on Information Systems 22(1), 89–115 (2004)CrossRefGoogle Scholar
  30. 30.
    Höök, K., Benyon, D., Munro, A.: Footprints in the snow. In: Höök, K., Benyon, D., Munro, A. (eds.) Social Navigation of Information Space, Springer, London (2003)Google Scholar
  31. 31.
    Johnson, S.C.: Hierarchical Clustering Schemes. Psychometrika 32(3), 241–254 (1967)CrossRefGoogle Scholar
  32. 32.
    Karypis, G.: Evaluation of Item-Based Top-N Recommendation Algorithms. 10th Conference of Information and Knowledge Management, CIKM, pp. 247–254 (2001)Google Scholar
  33. 33.
    Kobsa, A.: Privacy-Enhanced Web Personalization. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. LNCS, vol. 4321, pp. 620–670. Springer, Heidelberg (2007)Google Scholar
  34. 34.
    Konstan, J.A., Miller, B., Maltz, D., Herlocker, J., Gordon, L., Riedl, J.: GroupLens: Applying Collaborative Filtering To Usenet News. Communications of the ACM 40(3), 77–87Google Scholar
  35. 35.
    Lam, S.K., Riedl, J.: Shilling Recommender Systems For Fun And Profit. In: Proceedings of the 13th international conference on World Wide Web, pp. 393–402. ACM Press, New York (2004)CrossRefGoogle Scholar
  36. 36.
    Lam, S.K., Frankowski, D., Riedl, J.: Do You Trust Your Recommendations? An Exploration Of Security And Privacy Issues In Recommender Systems. In: Proceedings of the 2006 International Conference on Emerging Trends in Information and Communication Security. ETRICS, Freiburg, Germany, pp. 14–29 (2006)Google Scholar
  37. 37.
    Lin, W.: Association Rule Mining for Collaborative Recommender Systems. Master’s Thesis, Worcester Polytechnic Institute (May 2000)Google Scholar
  38. 38.
    Linden, G., Smith, B., York, J.: Amazon.Com Recommendations: Item-To-Item Collaborative Filtering. IEEE Internet Computing 7(1), 76–80Google Scholar
  39. 39.
    Ludford, P.J., Cosley, D., Frankowski, D., Terveen, L.: Think Different: Increasing Online Community Participation Using Uniqueness And Group Dissimilarity. In: Proceedings of the SIGCHI conference on Human factors in computing systems, Vienna, Austria, pp. 631–638. ACM Press, New York (2004)Google Scholar
  40. 40.
    MacQueen, J.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)Google Scholar
  41. 41.
    McLaughlin, M., Herlocker, J.: A Collaborative Filtering Algorithm and Evaluation Metric that Accurately Model the User Experience. In: Proceedings of the SIGIR Conference on Research and Development in Information Retrieval, pp. 329-336 (2004)Google Scholar
  42. 42.
    Maltz, D., Ehrlich, E.: Pointing The Way: Active Collaborative Filtering. In: Proceedings of ACM CHI’95 Conference on Human Factors in Computing Systems, pp. 202–209. ACM Press, New York (1995)Google Scholar
  43. 43.
    Miller, B.N., Konstan, J.A., Riedl, J.: Pocketlens: Toward A Personal Recommender System. ACM Trans. Inf. Syst. 22(3), 437–476 (2004)CrossRefGoogle Scholar
  44. 44.
    Mobasher, B.: Data Mining for Web Personalization. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. LNCS, vol. 4321, pp. 90–135. Springer, Heidelberg (2007)Google Scholar
  45. 45.
    Oard, D.W., Kim, J.: Implicit Feedback for Recommender Systems. In: Proceedings of the AAAI Workshop on Recommender Systems, Madison, Wisconsin (1998)Google Scholar
  46. 46.
    O’Connor, M., Cosley, D., Konstan, J.A., Riedl, J.: PolyLens: A Recommender System for Groups of Users. In: Proceedings of ECSCW 2001, Bonn, Germany, pp. 199–218 (2001)Google Scholar
  47. 47.
    O’Mahoney, M.P., Hurley, N., Kushmerick, N., Silvestre, G.: Collaborative Recommendation: A Robustness Analysis. ACM Transactions on Internet Technology 4(3), 344–377 (2003)Google Scholar
  48. 48.
    Pazzani, M., Billsus, D.: Content-based Recommendation Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)Google Scholar
  49. 49.
    Popescul, A., Ungar, L.H., Pennock, D.M., Lawrence, S.: Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments, pp. 437–444 (2001)Google Scholar
  50. 50.
    Ramakrishnan, N., Keller, B.K., Mirza, B.J.: Privacy Risks in Recommender Systems. IEEE Internet Computing, 54-62 (2001)Google Scholar
  51. 51.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An Open Architecture For Collaborative Filtering Of Netnews. In: Proceedings of the 1994 ACM conference on Computer supported cooperative work, Chapel Hill, North Carolina, pp. 175–186. ACM Press, New York (1994)CrossRefGoogle Scholar
  52. 52.
    Sarwar, B., Karypis, G., Konstan, J.A., Riedl, J.: Item-Based Collaborative Filtering Recommendation Algorithms. In: Proceedings of the 10th international conference on World Wide Web, Hong Kong, pp. 285–295. ACM Press, New York (2001)CrossRefGoogle Scholar
  53. 53.
    Sarwar, B., Karypis, G., Konstan, J.A., Riedl, J.: Incremental SVD-Based Algorithms for Highly Scaleable Recommender Systems. Proceedings of the Fifth International Conference on Computer and Information Technology (2002)Google Scholar
  54. 54.
    Sarwar, B., Karypis, G., Konstan, J.A., Riedl, J.: Application of Dimensionality Reduction in Recommender System–A Case Study. ACM WebKDD 2000 Web Mining for E-Commerce Workshop, Boston, Massachusetts (2000)Google Scholar
  55. 55.
    Schafer, J.B., Konstan, J.A., Riedl, J.: Meta-Recommendation Systems: User-Controlled Integration Of Diverse Recommendations. In: Proceedings of the Eleventh International Conference on Information And Knowledge Management, McLean, Virginia, pp. 43–51. ACM Press, New York (2002)CrossRefGoogle Scholar
  56. 56.
    Schein, A.I., Popescul, A., Ungar, L.H.: Generative Models for Cold-Start Recommendations. In: Proceedings of the Twenty-third Annual International ACM SIGIR Workshop on Recommender Systems, New Orleans, Louisiana, ACM, New York (2001)Google Scholar
  57. 57.
    Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating “Word of Mouth”, pp. 210–217. ACM, New York (1995)Google Scholar
  58. 58.
    Smyth, B.: Case-based Recommendation. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. LNCS, vol. 4321, pp. 342–376. Springer, Heidelberg (2007)Google Scholar
  59. 59.
    Swearingen, K., Sinha, R.: Beyond Algorithms, An HCI perspective on Recommender Systems. In: 2001 SIGIR Workshop on Recommender Systems, New Orleans (2001)Google Scholar
  60. 60.
    Torres, R., McNee, S.M., Abel, M., Konstan, J.A., Riedl, J.: Enhancing Digital Libraries With Techlens+. In: Proceedings of the 4th ACM/IEEE-CS joint conference on Digital Libraries, Tuscon, AZ, USA, pp. 228–236. ACM Press, New York (2004)Google Scholar
  61. 61.
    Ungar, L.H., Foster, D.P.: Clustering Methods for Collaborative Filtering. In: Proceedings of the 1998 Workshop on Recommender Systems, AAAI Press, Menlo Park (1998)Google Scholar
  62. 62.
    Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving Recommendation Lists Through Topic Diversification. In: Proceedings of the Fourteenth International World Wide Web Conference, WWW2005, pp. 22–32 (2005)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • J. Ben Schafer
    • 1
  • Dan Frankowski
    • 2
  • Jon Herlocker
    • 3
  • Shilad Sen
    • 2
  1. 1.Department of Computer Science, University of Northern Iowa, Cedar Falls, IA 50614-0507 
  2. 2.Department of Computer Science, University of Minnesota, 4-192 EE/CS Building, 200 Union St. SE, Minneapolis, MN 55455 
  3. 3.School of Electrical Engineering and Computer Science, Oregon State University, 102 Dearborn Hall, Corvallis, OR 97331 

Personalised recommendations