Collaborative filtering supporting web site navigation

AI Communications

Volumn 17, Issue 3, 2004, Pages 155-166

  Greco, Gianluigi ^a   Greco, Sergio ^a   Zumpano, Ester ^a  

^a UNIVERSITY OF CALABRIA   (Italy)

Author keywords

Adaptative Web Sites; Browsing; Collaborative Filtering; Web Searching

Indexed keywords

ALGORITHMS; GRAPH THEORY; INFORMATION RETRIEVAL; LEARNING SYSTEMS; NAVIGATION; WEB BROWSERS;

ADAPTIVE WEN SITES; BROWSING; COLLABORATIVE FILTERING; PERSONALIZATION; WEB SEARCHING;

WEBSITES;

EID: 21844469214     PISSN: 09217126     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (29)

1. Y. Azar, A. Fiat, A.R. Karlin, F. McSherry and J. Saia, Spectral analysis of data, in: ACM Symp. of Theory of Computer Science, 2001, pp. 619-626.
2. M. Balabanovic and Y. Shoham, Content-based, collaborative recommendation, Communication of the ACM 40(3) (1997), 66-72.
3. S. Brin and L. Page, The PageRank citation ranking: bringing order to the Web, http://google.standford.edu/backrub/pageranksub.ps.
4. W.W. Cohen and W. Fan, Web-collaborative filtering: recommending music by crawling the Web, Proc. Int. Conf. on World Wide Web, Computer Networks 33(1-6) (2000), 685-698.
5. J. Fink, A. Kobsa and A. Nill, Adaptable and adaptive information provision for all users, including disabled and elderly people, The New Review of Hypermedia and Multimedia 4 (1998), 163-188.
6. E.J. Glover, S. Lawrence, W.P. Birmingham and C. Lee Giles, Architecture of a metasearch engine that supports user information needs, in: Proc. Int. Conf. on Information Knowledge Management, 1999, pp. 210-216.
7. D. Goldberg, D. Nichols, B.M. Oki and D.B. Terry, Using collaborative filtering to weave an information tapestry, Communications of the ACM 35(12) (1992), 61-70.
8. N. Good, B. Schafer, J. Konstan, A. Borchers, B. Sarwar, J. Herlocker and J. Riedl, Combining collaborative filtering with personal agents for better recommendations, in: Proc. of the AAAI'99 Conference, 1999, pp. 439-446.
9. Google coorporation, Google Search Engine, http://www.google.com.
10. G. Greco, S. Greco and E. Zumpano, A probabilistic approach for distillation and ranking of Web pages, WWW Journal 4(3) (2001), 189-207.
11. S.J. Hong, R. Natarajan and I. Belitskaya, A new approach for item choice recommendations, in: Third Int. Conf. on Data Warehousing and Knowledge Discovery, 2001, pp. 131-140.
12. T. Joachims, D. Freitag and T.M. Mitchell, Web Watcher: a tour guide for the world Wide Web, in: Proc. Int. Joint Conf. on Artificial Intelligence, Vol. 1, 1997, pp. 770-777.
13. G. Karypis, Evaluation of item-based Top-N recommendation algorithms, in: Proc. Int. Conf. on Information and Knowledge Management, 2001, pp. 247-254.
14. J.M. Kleinberg, Authoritative sources in a hyperlinked environment, Journal of the ACM 46(5) (1999), 604-632.
15. J.A. Konstan, B.N. Miller, D. Maltz, J.L. Herlocker, L.R. Gordon and J. Riedl, GroupLens: applying collaborative filtering to usenet news, Communication of the ACM 40(3) (1997), 77-87.
16. R. Kumar, P. Raghavan, S. Rajagopalan and A. Tomkins, Recommendation systems: a probabilistic analysis, Journal of Computer and System Sciences 63(1) (2001), 42-61.
17. S. Lawrence, Context in Web search, IEEE Data Engineering Bulletin 23(3) (2000), 25-32.
18. R.D. Lawrence, G. Almasi, V. Kotlyar, M. Viveros and S. Duri, Personalization of supermarket product recommendations, Data Mining and Knowledge Discovery 5 (2001), 11-32.
19. H. Lieberman, Letizia: An agent that assists Web browsing, in: Proc. Int. Joint Conf. on Artificial Intelligence, Vol. 1, 1995, pp. 924-929.
20. D.M. Pennock, E. Horvitz, S. Lawrence and C. Lee Giles, Collaborative filtering by personality diagnosis: a hybrid memory-and model-based approach, in: Proc. 6th Conf. on Uncertainty in Artificial Intelligence, 2000, pp. 473-480.
21. M. Perkowitz and O. Etzioni, Towards adaptive Web sites: conceptual framework and case study, Artificial Intelligence 118(1-2) (2000), 245-275.
22. N. Ramakrishnan, B.J. Keller, B.J. Mirza, A.Y. Grama and G. Karypis, When being weak is brave: privacy issues in recommender systems, IEEE Internet Computing (2002) (to appear).
23. J. Riedl, Recommender systems in commerce and community, in: Proc. 7th ACM SIGKDD Int. Conf. on Knowledge discovery and Data Mining, 2001, p. 15.
24. J.J. Rocchio, Relevance feedback in information retrieval, in: The SMART Retrieval System-Experiments in Automatic Document Processing, Englewood Cliffs, NJ, Prentice Hall, Inc., 1971, pp. 313-323.
25. B.M. Sarwar, G. Karypis, J.A. Konstan and J. Riedl, Analysis of recommendation algorithms for e-commerce, in: ACM Conference on Electronic Commerce, 2000, pp. 158-167.
26. B.M. Sarwar, G. Karypis, J.A. Konstan and J. Riedl, Item-based collaborative filtering recommendation algorithms, in: Proc. 10th Int. Conf. on World Wide Web, 2001, pp. 285-295.
27. C. Shahabi, F.B. Kashani, Y. Chen and D. McLeod, Yoda: an accurate and scalable Web-based recommendation system, in: Proc. 9th Int. Conf. on Cooperative Information Systems, 2001, pp. 418-432.
28. U. Shardanand and P. Maes, Social information filtering: Algorithms for automatic "word of mouth", in: Proc. Conf. on Human Factors in Computing Systems, 1995, pp. 210-217.
29. A. Wexelblat and P. Maes, Footprints: history-rich tools for information foraging, in: Proc. Conf. on Human Factors in Computing Systems, 1999, pp. 270-277.