User Modeling and User-Adapted Interaction

, Volume 12, Issue 4, pp 331–370 | Cite as

Hybrid Recommender Systems: Survey and Experiments

  • Robin Burke
Article

Abstract

Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.

case-based reasoning collaborative filtering recommender systems 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alspector, J., Koicz, A., and Karunanithi, N.: 1997, ‘Feature-based and Clique-based User Models for Movie Selection: A Comparative Study’. User Modelinga and User-Adapted Interaction 7, 279-304.CrossRefGoogle Scholar
  2. Avery, C. and Zeckhauser, R.: 1997, ‘Recommender systems for evaluating computer messages’. Communications of the ACM 40(3), 88-89.CrossRefGoogle Scholar
  3. Balabanovic, M.: 1998, ‘Exploring versus Exploiting when Learning User Models for Text Representation’. User Modelinga nd User-Adapted Interaction 8(1-2), 71-102.CrossRefGoogle Scholar
  4. Balabanovic, M.: 1997, ‘An Adaptive Web Page Recommendation Service’. In: Agents 97: Proceedings of the First International Conference on Autonomous Agents, Marina Del Rey, CA, pp. 378-385.Google Scholar
  5. Basu, C., Hirsh, H. and Cohen W.: 1998, ‘Recommendation as Classification: Using Social and Content-Based Information in Recommendation’. In: Proceedings of the 15th National Conference on Artificial Intelligence, Madison, WI, pp. 714-720.Google Scholar
  6. Belkin, N. J. and Croft, W. B.: 1992, ‘Information Filtering and Information Retrieval: Two Sides of the Same Coin?’ Communications of the ACM 35(12), 29-38.CrossRefGoogle Scholar
  7. Billsus, D. and Pazzani, M.:2000. ‘User Modeling for Adaptive News Access’. User-Modeling and User-Adapted Interaction 10(23), 147-180.CrossRefGoogle Scholar
  8. Breese, J. S., Heckerman, D. and Kadie, C.: 1998, ‘Empirical analysis of predictive algorithms for collaborative filtering’. In: Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence, pp. 43-52.Google Scholar
  9. Brin, S. and Page, L.: 1998, ‘The anatomy of a large-scale hypertextual {Web} search ngine’. Computer Networks and ISDN Systems, 30(1-7), 107-117.CrossRefGoogle Scholar
  10. Burke, R.: 1999a, ‘The Wasabi Personal Shopper: A Case-Based Recommender System’. In: Proceedings of the 11th National Conference on Innovative Applications of Artificial Intelligence, pp. 844-849.Google Scholar
  11. Burke, R.: 1999b, ‘Integrating Knowledge-Based and Collaborative-Filtering Recommender Systems’. In: Artificial Intelligence for Electronic Commerce: Papers from the AAAI Workshop (AAAI Technical Report WS-99-0 1), pp. 69-72.Google Scholar
  12. Burke, R.: 2000, ‘Knowledge-based Recommender Systems’. In: A. Kent (ed.): Encyclopedia of Library and Information Systems. Vol. 69, Supplement 32.Google Scholar
  13. Burke, R., Hammond, K., and Young, B.: 1997, ‘The FindMe Approach to Assisted Browsing’. IEEE Expert, 12(4), 32-40.CrossRefGoogle Scholar
  14. Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D. and Sartin, M.: 1999, ‘Combining Content-Based and Collaborative Filters in an Online Newspaper’. SIGIR ‘99 Workshop on Recommender Systems: Algorithms and Evaluation. Berkeley, CA. <URL: http://www.cs.umbc.edu/ ~ian/sigir99-rec/papers/claypool_m.ps.gz>Google Scholar
  15. Condliff, M. K., Lewis, D. D., Madigan, D. and Posse, C.: 1999, ‘Bayesian Mixed-Effects Models for Recommender Systems’. SIGIR ‘99 Workshop on Recommender Systems: Algorithms and Evaluation. Berkeley, CA. <URL: http://www.cs.umbc.edu/ ~ian/sigir99-rec/papers/condliff_m.ps.gz>Google Scholar
  16. Dron, J., Mitchell, R., Siviter, P. and Boyne, C.: 1999, ‘CoFIND-an Experiment in N-dimensional Collaborative Filtering’. In: Proceedings of WebNet ‘99. Association for the Advancement of Computing in Education.Google Scholar
  17. Foltz, P. W.: 1990, ‘Using Latent Semantic Indexing for Information Filtering’. In: R. B. Allen (ed.): Proceedings of the Conference on Office Information Systems, Cambridge, MA, pp. 40-47.Google Scholar
  18. Guttman, Robert H.: 1998, ‘Merchant Differentiation through Integrative Negotiation in Agent-mediated Electronic Commerce’. Master’s Thesis, School of Architecture and Planning, Program in Media Arts and Sciences, Massachusetts Institute of Technology.Google Scholar
  19. Guttman, R. H., Moukas, A. G. and Maes, P.: 1998, ‘Agent-Mediated Electronic Commerce: A Survey’. Knowledge Engineering Review, 13(2), 147-159.CrossRefGoogle Scholar
  20. Hill, W., Stead, L., Rosenstein, M. and Furnas, G.: 1995, ‘Recommending and evaluating choices in a virtual community of use’. In: CHI ‘95: Conference Proceedings on Human Factors in Computing Systems, Denver, CO, pp. 194-201.Google Scholar
  21. Jennings, A. and Higuchi, H.: 1993, ‘A User Model Neural Network for a Personal News Service.’ User Modelingand User-Adapted Interaction, 3, 1-25.CrossRefGoogle Scholar
  22. Kolodner, J.: 1993, ‘Case-Based Reasoning’. San Mateo, CA: Morgan Kaufmann.Google Scholar
  23. Konstan, J. A., Riedl, J., Borchers, A. and Herlocker, J. L.: 1998, ‘Recommender Systems: A GroupLens Perspective.’ In: Recommender Systems: Papers from the 1998 Workshop (AAAI Technical Report WS-98-08). Menlo Park, CA: AAAI Press, pp. 60-64Google Scholar
  24. Krulwich, B.: 1997, ‘Lifestyle Finder: Intelligent User Profiling Using Large-Scale Demographic Data’. Artificial Intelligence Magazine 18(2), 37-45.Google Scholar
  25. Lang, K.: 1995, ‘Newsweeder: Learning to filter news’. In: Proceedings of the 12th International Conference on Machine Learning, Lake Tahoe, CA, pp. 331-339.Google Scholar
  26. Littlestone, N. and Warmuth, M.: 1994, ‘The Weighted Majority Algorithm’. Information and Computation 108(2), 212-261.CrossRefGoogle Scholar
  27. Mooney, R. J. and Roy, L.: 1999, ‘Content-Based Book Recommending Using Learning for Text Categorization’. SIGIR ‘99 Workshop on Recommender Systems: Algorithms and Evaluation. Berkeley, CA. <URL: http://www.cs.umbc.edu/ ~ian/sigir99-rec/papers/mooney_r.ps.gz>Google Scholar
  28. Nichols, D. M: 1997, ‘Implicit Rating and Filtering’. In Proceedings of the Fifth DELOS Workshop on Filteringan d Collaborative Filtering, Budapest, Hungary, pp. 31-36, ERCIM.Google Scholar
  29. Pazzani, M. J.: 1999, ‘A Framework for Collaborative, Content-Based and Demographic Filtering’. Artificial Intelligence Review, 13(5/6), 393-408.CrossRefGoogle Scholar
  30. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. and Riedl, J.: 1994, ‘GroupLens: An Open Architecture for Collaborative Filtering of Netnews’. In: Proceedings of the Conference on Computer Supported Cooperative Work, Chapel Hill, NC, pp. 175-186.Google Scholar
  31. Resnick, P. and Varian, H. R.: 1997, ‘Recommender Systems’. Communications of the ACM, 40(3), 56-58.CrossRefGoogle Scholar
  32. Rich, E.: 1979, ‘User Modeling via Stereotypes’. Cognitive Science 3, 329-354.CrossRefGoogle Scholar
  33. Richter, M: 1995, ‘The knowledge contained in similarity measures’. Invited talk at the International Conference on CaseBased Reasoning, Sesimbra, Portugal, October 25. Summary available at <URL: http://www.cbr-web.org/documents/Richtericcbr95remarks.html>Google Scholar
  34. Rocchio, Jr., J.: 1971, ‘Relevance feedback in information retrieval’. In The SMART System Experiments in Automatic Document Processing, New York: Prentice Hall, pp. 313-323.Google Scholar
  35. Rosenstein, M. and Lochbaum, C.: 2000, ‘Recommending from Content: Preliminary Results from an E-Commerce Experiment.’ In: Proceedings of CHI’00: Conference on Human Factors in Computing, The Hague, Netherlands.Google Scholar
  36. Salton, G., and McGill, M.: 1983, ‘Introduction to modern information retrieval’. New York: McGraw-Hill.Google Scholar
  37. Sarwar, B. M., Konstan, J. A., Borchers, A., Herlocker, J. Miller, B. and Riedl, J.: 1998, ‘Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System’. In: Proceedings of the ACM 1998 Conference on Computer Supported Cooperative Work, Seattle, WA, pp. 345-354.Google Scholar
  38. Schafer, J. B., Konstan, J. and Riedl, J.: 1999, ‘Recommender Systems in E-Commerce’. In: EC ‘99: Proceedings of the First ACM Conference on Electronic Commerce, Denver, CO, pp. 158-166.Google Scholar
  39. Schmitt, S. and Bergmann, R.: 1999, ‘Applying case-based reasoning technology for product selection and customization in electronic commerce environments.’ 12th Bled Electronic Commerce Conference. Bled, Slovenia, June 7-9, 1999.Google Scholar
  40. Schwab, I., Kobsa, A. and Koychev, I.: 2001, ‘Learning User Interests through Positive Examples Using Content Analysis and Collaborative Filtering’. Internal Memo, GMD, St. Augustin, GermanyGoogle Scholar
  41. Shardanand, U. and Maes, P.: 1995, ‘Social Information Filtering: Algorithms for Automating “Word ofMouth’. In: CHI ‘95: Conference Proceedings onHuman Factors in Computing Systems, Denver, CO, pp. 210-217.Google Scholar
  42. Smyth, B. and Cotter, P.: 2000, ‘A Personalized TV Listings Service for the Digital TV Age’. Knowledge-Based Systems 13: 53-59.CrossRefGoogle Scholar
  43. Strang, G.: 1988, Linear Algebra and Its Applications, New York: Harcourt Brace.Google Scholar
  44. Terveen, L. and Hill, W: 2001, ‘Human-Computer Collaboration in Recommender Systems’. In: J. Carroll (ed.): Human Computer Interaction in the New Millenium. New York: Addison-Wesley, 487-509.Google Scholar
  45. Towle, B. and Quinn, C.: 2000, ‘Knowledge Based Recommender Systems Using ExplicitUser Models’. In Knowledge-Based Electronic Markets, Papers from the AAAI Workshop, AAAI Technical Report WS-00-04. pp. 74-77. Menlo Park, CA: AAAI Press.Google Scholar
  46. Tran, T. and Cohen, R.: 2000, ‘Hybrid Recommender Systems for Electronic Commerce’. In Knowledge-Based ElectronicMarkets, Papers from the AAAIWorkshop, AAAI Technical Report WS-00-04. pp. 78-83. Menlo Park, CA: AAAI Press.Google Scholar
  47. U.S. Information Technology Industry Council, ‘The Protection of Personal Data in Electronic Commerce’, Public Policy Document, Nov. 20, 1997. <URL: http://www.itic.org/iss_pol/ppdocs/pp-privprin.html>Google Scholar
  48. Wasfi, A. M.: 1999, ‘Collecting User Access Patterns for Building User Profiles and Collaborative Filtering’. In: IUI “99: Proceedings of the 1999 International Conference on Intelligent User Interfaces, Redondo Beach, CA, pp. 57-64.Google Scholar
  49. Zukerman, I. and Albrecht, D.: 2001, ‘Predictive Statistical Models for UserModeling’. User Modelingand User-Adapted Interaction 11(1-2), 5-18.CrossRefGoogle Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Robin Burke
    • 1
  1. 1.Department of Information Systems and Decision SciencesCalifornia State UniversityFullertonUSA

Personalised recommendations