skip to main content
article

Ontological user profiling in recommender systems

Published:01 January 2004Publication History
Skip Abstract Section

Abstract

We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. The overall performance of our ontological recommender systems are also presented and favourably compared to other systems in the literature.

References

  1. Alani, H., Dasmahapatra, S., O'hara, K., and Shadbolt, N. 2003. ONTOCOPI---Using ontology-based network analysis to identify communities of practice. IEEE Intell. Syst. 18, 2, 18--25.]] Google ScholarGoogle Scholar
  2. Aha, D., Kibler, D., and Albert, M. 1991. Instance-based learning algorithms. Mach. Learn. 6, 37--66.]] Google ScholarGoogle Scholar
  3. Balabanović, M. and Shoham, Y. 1997. Fab: Content-based, collaborative recommendation. Commun. ACM 40, 3, 67--72.]] Google ScholarGoogle Scholar
  4. Billsus, D. and Pazzani, M. J. 2000. User modelling for Adaptive News Access. In User Model. User-Adapt. Interact. 10, 147--180.]] Google ScholarGoogle Scholar
  5. Bollacker, K. D., Lawrence, S., and Giles, C. L. 1998. CiteSeer: An autonomous web agent for automatic retrieval and identification of interesting publications. In Autonomous Agents 98 (Minneapolis, Minn.).]] Google ScholarGoogle Scholar
  6. Budzik, J., Hammond, K., and Birnbaum, L. 2001. Information access in context. Knowl. Based Syst. 14 (1--2), 37--53.]]Google ScholarGoogle Scholar
  7. Burke, R. 2000. Knowledge-based recommender systems. In Encyclopaedia of Library and Information Systems, vol. 69, Supplement 32. A. Kent, Ed.]]Google ScholarGoogle Scholar
  8. Claypool, M., Gokhale, A., and Miranda, T. 1999. Combining content-based and collaborative filters in an online newspaper. In Proceedings of the 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'99) (Berkeley, Calif.). ACM, New York.]]Google ScholarGoogle Scholar
  9. Craven, M., Dipasquo, D., Freitag, D., McCallum, A., Mitchell, T., Nigam K., and Slattery, S. 1998. Learning to extract symbolic knowledge from the world wide web. In Proceedings of the 15th National Conference on Artificial Intelligence (AAAI-98).]] Google ScholarGoogle Scholar
  10. Delgado, J., Ishii, N., and Ura, T. 1998. Intelligent collaborative information retrieval. In Proceedings of Artificial Intelligence (IBERAMIA'98). Lecture Notes in Artificial Intelligence Series No. 1484.]] Google ScholarGoogle Scholar
  11. Eriksson, H., Fergeson, R., Shahr, Y., and Musen, M. 1999. Automatic generation of ontology editors. In Proceedings of the 12th Workshop on Knowledge Acquisition, Modelling, and Management (KAW'99) (Ban, Alberta, Canada).]]Google ScholarGoogle Scholar
  12. Freund, Y. and Schapire, R. E. 1996. Experiments with a new boosting algorithm. In Proceedings of the 13th International Conference on Machine Learning.]]Google ScholarGoogle Scholar
  13. Guarino, N. and Giaretta, P. 1995. Ontologies and knowledge bases: Towards a terminological clarification. In Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing, N. Mars, Ed. IOS Press, 25--32.]]Google ScholarGoogle Scholar
  14. Guarino, N., Masolo, C., and Vetere, G. 1999. OntoSeek: Content-based access to the web. IEEE Intell. Syst. 14, 3.]] Google ScholarGoogle Scholar
  15. Kobsa, A. 1993. User modeling: Recent work, prospects and Hazards. In Adaptive User Interfaces: Principles and Practice, M. Schneider-Hufschmidt, T. Kühme, and U. Malinowski, Eds. North-Holland, Amsterdam, The Netherlands.]]Google ScholarGoogle Scholar
  16. Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., and Riedl, J. 1997. GroupLens: Applying collaborative filtering to usenet news. Commun. ACM 40, 3, 77--87.]] Google ScholarGoogle Scholar
  17. Lang, K. 1995. NewsWeeder: Learning to filter NetNews. In ICML95 Conference Proceedings, 331--339.]]Google ScholarGoogle Scholar
  18. Larkey, L. S. 1998. Automatic essay grading using text categorization techniques. In Proceedings of SIGIR-98, 21st ACM International Conference on Research and Development in Information Retrieval (Melbourne, Australia).]] Google ScholarGoogle Scholar
  19. Maltz, D. and Ehrlich, E. 1995. Pointing the way: Active collaborative filtering. In Proceedings of the CHI'95 Human Factors in Computing Systems. ACM. New York.]] Google ScholarGoogle Scholar
  20. McCallum, A. K., Nigam, K., Rennie, J., and Seymore, K. 2000. Automating the construction of internet portals with machine learning. Inf. Retri. 3, 2, 127--163.]] Google ScholarGoogle Scholar
  21. Melville, P., Mooney, R. J., and Nagarajan, R. 2002. Content-boosted collaborative filtering for improved recommendations. In Proceedings of the 18th National Conference on Artificial Intelligence (AAAI-2002) (Edmonton, Ont., Canada).]] Google ScholarGoogle Scholar
  22. Middleton, S. E., Alani, H., Shadbolt, N. R., and de Roure, D. C. 2002. Exploiting synergy between ontologies and recommender systems. In International Workshop on the Semantic Web, Proceedings of the 11th International World Wide Web Conference WWW-2002 (Hawaii).]]Google ScholarGoogle Scholar
  23. Middleton, S. E., de Roure, D. C., and Shadbolt, N. R. 2001. Capturing knowledge of user preferences: Ontologies on recommender systems. In Proceedings of the 1st International Conference on Knowledge Capture (K-CAP 2001) (Victoria, B.C., Canada).]] Google ScholarGoogle Scholar
  24. Mladenić, D. 1996. Personal WebWatcher: Design and implementation. Tech. Rep. IJS-DP-7472, Department for Intelligent Systems, J. Stefan Institute.]]Google ScholarGoogle Scholar
  25. Mladenić, D. and Stefan, J. 1999. Text-learning and related intelligent agents: A survey. IEEE Intell. Syst. 44--54.]] Google ScholarGoogle Scholar
  26. Nwana, H. 1996. Software agents: An overview. The Knowl. Eng. Rev. 11, 3, 205--244.]]Google ScholarGoogle Scholar
  27. O'hara, K., Shadbolt, N., and Buckingham Shum, S. 2001. The AKT Manifesto. http: www.aktors.org/publications/manifesto.pds.]]Google ScholarGoogle Scholar
  28. Porter, M. 1980. An algorithm for suffix stripping. Program. 14, 3, 130--137.]]Google ScholarGoogle Scholar
  29. Rashid, A., Albert, I., Cosley, D., Lam, S. K., Mcnee, S. M., Konstan, J. A., and Riedl, J. 2002. Getting to know you: Learning new user preferences in recommender systems. In Proceedings of the IUI'02 (San Francisco, Calif.).]] Google ScholarGoogle Scholar
  30. Schein, A. L., Popescul, A., and Ungar, L. H. 2002. Methods and metrics for cold-start recommendations. In Proceedings of the SIGIR'02 (Tampere, Finland).]] Google ScholarGoogle Scholar
  31. Sebastiani, F. 2002. Machine learning in automated text categorization. ACM Comput. Surv.]] Google ScholarGoogle Scholar
  32. Shadbolt, N., O'hara, K., and Crow, L. 1999. The experimental evaluation of knowledge acquisition techniques and methods: history, problems and new directions. Int. J. Hum.-Comput. Stud. 51, 729--755.]] Google ScholarGoogle Scholar
  33. Smart Staff 1974. User's Manual for the SMART Information Retrieval System. Tech. Rep. 71--95, Cornell University.]]Google ScholarGoogle Scholar

Index Terms

  1. Ontological user profiling in recommender systems

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in

            Full Access

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader