The role of semantic relevance in dynamic user community management and the formulation of recommendations

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2348, Issue , 2002, Pages 200-215

  Papadopoulos, Nick ^a   Plexousakis, Dimitris ^a,b  

^a INSTITUTE OF COMPUTER SCIENCE   (Greece)

^b UNIVERSITY OF CRETE   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; INFORMATION SYSTEMS; SEMANTICS; SYSTEMS ENGINEERING;

CLASSIFICATION ALGORITHM; COMMUNITY MANAGEMENT; COMPARISON TECHNIQUES; DYNAMIC ADJUSTMENT; DYNAMIC MANAGEMENT; RECOMMENDATION ALGORITHMS; SEMANTIC RELEVANCE; USER COMMUNITIES;

DYNAMICS;

EID: 84875970297     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (26)

1. D. H. Widyantoro, T. R. Ioerger, and J. Yen.. Learning User Interest Dynamics with, Three-Descriptor Representation.. Journal of the American Society for In- formation Science (JASIS), 2000.
2. M. Balabanovic and Y. Sholam..Combining Content-Based and Collaborative Recommendation.. Communications of the ACM, 1997.
3. G. Karypis..Evaluation of Item-Based Top-N Recommendation Algorithms., 2000. In Proceedings of CIKM, 2001, pp. 247-254, 2001.
4. M. Keim Condliff, D. D. Lewis, D. Madigan, and C. Posse..Baeysian Mixed- Effects Models for Recommendation Systems.. In Proceedings of ACM SIGIR Workshop on Recommendation Systems, 1999.
5. M. Newman, and D. Fisher..A News Article Recommendation System for Informavores., http://www.cs.berkeley.edu/~danyelf/inf/.
6. J. Konstant, B. Miller, D. Maltz, J. Herlocker, L. Gordon, and J. Riedl..Grouplens: applying collaborative filtering to Usenet news.. Communications of the ACM, 40(3):77-87, 1997.
7. P. Resnick, N. Iacovou, M. Suchak, P. Bergston, and J. Riedl.. Grouplens: An open architecture for collaborative filtering of netnews.. In Proceedings of CSCW, 1994.s
8. R. Baeza-Yates, B. Ribeiro-Neto. Modern Information Retrieval.. Addison Wesley, 1999.
9. T. Mitchell. Machine Learning.. NY:McGraw-Hill, 1997.
10. G. Widmer, and M. Kubat.Learning in the Presence of Concept Drift and Hidden Contexts.. Machine Learning Journal, 1, 69-101, 1996.
11. M. Pazzani and D. Billsus. Learning and revising user profiles: The identification of interesting web sites.. Machine Learning, 27:313-331, 1997.
12. D.H. Widyantoro. Learning User Profile in Personalized News Agent.. Master.s thesis, Department of Computer Science, Texas A&M University, 1999.
13. B. Mobasher, H. Dai, T. Luo, M. Nakagawa, and J. Witshire..Discovery of aggregate usage profiles for web personalization.. In Proceedings of the WebKDD Workshop, 2000.
14. P. Chan..A non-invasive learning approach to building web user profiles.. In Proceedings of ACM SIGKDD International Conference, 1999.
15. C. Buckley and G. Salton..Optimization of Relevance Feedback Weights.. In Proceedings of the 18th Annual Intl ACM SIGIR Conference, Seattle, 1995.
16. M. Balabanovic.. An Adaptive Web Page Recommendation Service.. In Pro- ceedings of First International Conference on Autonomous Agents, pp. 378-385, 1997
17. G. Salton, and M.J. McGill. Introduction to Modern Information Re- trieval. McGraw-Hill, 1983.
18. J. Allan. Incremental Relevance Feedback for Information Filtering.. In Proceedings of the Nineteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 270-278, ACM, 1996.
19. J. J. Rocchio, "Relevance feedback in information retrieval". In G. Salton, The SMART Retrieval System: Experiments in Automatic Document Processing, pp. 313-323, Englewood Cliffs, NJ, 1971. Prentice Hall, Inc.
20. M. Balabanovic..Learning to Surf: Multi-agent Systems for Adaptive Web Page Recommendation.. PhD thesis, Department of Computer Science, Stanford University, 1998.
21. D. Billsus and M. Pazzani..A personal news agent that talks, learns and explains.. In Proceedings of the third International Conference on Autonomous Agents (Agents '99), pages 268--275, Seattle, WA, 1999
22. B.D. Sheth, A Learning Approach to Personalized Information Filtering.. Master’s Thesis, Department of Electrical Engineering and Computer Science, MIT, 1994.
23. D.H. Widyantoro, T.R. Ioerger, and J. Yen. An Adaptive Algorithm for Learning Changes in User Interests.. In Proceedings of the Eight International Conference on Information and Knowledge Management, pp. 405-412, 1999.
24. C. Fox, "Lexical Analysis and Stoplists", in Information Retrieval: Data struc- tures and Algorithms, edited by W. B. Frakes and R. Baeza-Yates, 1992, Prentice- Hall, pp. 102-130.
25. E. Riloff, Little words can make a big difference for text classification.. In Proceedings, 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’95), pp. 130-136, Seattle, 1995.
26. WordNet, a lexical database for the English language, http://www.cogsci.princeton.edu/~wn/.