An approach for the capture of context-dependent document relationships extracted from Bayesian analysis of users' interactions with information

Information Retrieval

Volumn 10, Issue 2, 2007, Pages 115-141

  Campbell, D R ^a   Culley, S J ^a   McMahon, C A ^a   Sellini, F ^b  

^a University of Bath   (United Kingdom)

^b AIRBUS OPERATIONS LTD   (United Kingdom)

Author keywords

Context; Implicit indicators; Na ve Bayesian classification; Recommender systems

Indexed keywords

EID: 33846961754     PISSN: 13864564     EISSN: 15737659     Source Type: Journal    
DOI: 10.1007/s10791-006-9016-2     Document Type: Article

References (46)

1. Ashman, H. (2000). Electronic document addressing: Dealing with change. ACM Computing Surveys, 32(3), 201-212.
2. Beeferman, D., & Berger, A. (2000). Agglomerative clustering of a search engine query log. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Boston, Massachusetts, United States, August 20-23, 2000) (pp. 407-416), New York: NY. KDD'00. ACM Press.
3. Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1-7), 107-117.
4. Claypool, M., Le, P., Waseda, M., & Brown, D. (2001). Implicit interest indicators. In Proceedings of the 6th International Conference on Intelligent user Interfaces. (pp. 33-40).
5. Coppin, B. (2004). Artificial intelligence illuminated, 1st Edition, ISBN 0-7637-3230-3. (pp. 351-356).
6. Czerwinski, M., Horvitz, E., & Wilhite, S. (2004). A diary study of task switching and interruptions. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. (pp. 175-182).
7. Dougherty, J., Kohavi, R., & Sahami, M. (1995). Supervised and unsupervised discretization of continuous features. In Proceedings of the Twelfth International Conference on Machine Learning (pp. 194-202).
8. Drucker, P. F. (1959). The landmarks of tomorrow. London: Heinemann.
9. Dumais, S., Platt, J., Heckerman, D., & Sahami, M. (1998). Inductive learning algorithms and representations for text categorization. In Proceedings of the Seventh International Conference on Information and Knowledge Management. (pp. 148-155).
10. Geller, N. L. (1978). On the citation influence methodology of Pinski and Narin. Information Processing and Management, 14(2), 93-95.
11. Goldberg, D., Nichols, D., Oki, B., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61-70.
12. Hales, C. (1991). Analysis of the engineering design process in an industrial context. Hampshire, UK: Gants Hill Publications.
13. Haveliwala, T. H. (2002). Topic-sensitive PageRank. In Proceedings of the 11th International Conference on World Wide Web [(Honolulu, Hawaii, USA, May 07-11, 2002)]. pp. 517-526. WWW '02. ACM Press, New York: NY.
14. Herlocker, J. L., & Konstan, J. A. (2001) Content-independent task-focused recommendation. IEEE Internet Computing, 5(6), 40-47.
15. Herlocker, J. L., Konstan, J. A., & Riedl, J. (2002) An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information Retrieval, 5(4), 287-310.
16. Hill, W., Hollan, J., Wroblewski, D., & McCandless, T. (1992). Edit wear and read wear. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems. (pp. 3-9).
17. Hill, W., Stead, L., Rosenstein, M., & Furnas, G. (1995). Recommending and evaluating choices in a virtual community of use. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems [(Denver, Colorado, United States, May 07-11, 1995)] (pp. 194-201), New York: NY. ACM Press/Addison-Wesley Publishing Co.
18. Horvitz, E., Breese, J., Heckerman, D., Hovel, D., & Rommelse, K. (1998). The lumiere project: Bayesian user modelling for inferring the goals and needs of software users. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (pp. 256-265). Morgan Kaufmann, San Francisco. Madison, WI.
19. Jeh, G., & Widom, J. (2002). SimRank: a measure of structural-context similarity. In Proceedings of the Eighth ACM SIGKDD international Conference on Knowledge Discovery and Data Mining. [(Edmonton, Alberta, Canada, July 23-26, 2002)] (pp. 538-543). ACM Press, New York: NY.
20. Joachims, T., Freitag, D., & Mitchell, T. (1997). WebWatcher: A tour guide for the World Wide Web. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI97). (pp. 770-777). Nagoya, Japan.
21. Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In Proceedings of ECML-98, 10th European Conference on Machine Learning [(Chemnitz, Germany, 1998)]. (pp. 137-142).
22. Kelly, D., & Belkin, N. J. (2001). Reading time, scrolling and interaction: exploring implicit sources of user preferences for relevance feedback. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval [(New Orleans, Louisiana, United States)]. SIGIR '01 (pp. 408-409) New York: ACM Press.
23. Kelly, D., & Belkin, N. J. (2004). Display time as implicit feedback: Understanding task effects. In Proceedings of the 27th Annual international ACM SIGIR Conference on Research and Development in Information Retrieval [(Sheffield, United Kingdom, July 25-29, 2004)]. (pp. 377-384). SIGIR '04. ACM Press.
24. Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), 604-632.
25. Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., & Riedl, J. (1997). GroupLens: Applying collaborative filtering to usenet news. Communications of the ACM, 40(3), 77-87.
26. Lewis, D. (1998). Naive (Bayes) at forty: The independence assumption in information retrieval. In Proceedings of ECML-98, 10th European Conference on Machine Learning. (pp. 4-15).
27. Lieberman, H. (1995). Letizia: an agent that assists web browsing. In Proceedings of the 14th International Joint Conference on Artificial Intelligence. (pp. 475-480).
28. Lowe, A. (2002). Studies of information use by engineering designers and the development of strategies to aid in its classification and retrieval. PhD thesis, University of Bath, UK, Chapter 3.
29. Morita, M., & Shinoda, Y. (1996). Information filtering based on user behaviour analysis and best match text retrieval. In Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. (pp. 272-281).
30. Oard, D. W., & Kim, J. (1998). Implicit feedback for recommender systems. In Proceedings of the AAAI Workshop on Recommender Systems. (pp. 80-82). Madison, WI, USA.
31. O'Riordan, C., & Sorensen, H. (2002) Information Filtering and Retrieval: An Overview. Technical Report, Department of Information Technology, NUI Galway, Ireland.
32. Pitkow, J. E. (1999). Summary of WWW characterizations. World Wide Web, 2(1-2), 3-13.
33. Resnik, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM, Conference on Computer Supported Cooperative Work. (pp. 175-186).
34. Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34(1), 1-47.
35. Seo, Y. W., & Zhang, B. T. (2000). Learning user's preferences by analyzing wed-browsing behaviours. In Proceedings of the ACM 4th International Conference on Autonomous Agents. (pp. 381-387).
36. Shardanand, U., & Maes, P. (1995). Social information filtering: Algorithms for automating 'Word of Mouth'. In Proceedings of the Conference on Human Factors in Computing Systems. (pp. 210-217). New York: NY. ACM Press/Addison-Wesley Publishing Co.
37. Smith, G., Baudisch, P., Roverson, G., Czerwinski, M., Meyers, B., Robbins, D., & Andrews, D. (2003). GroupBar: The taskbar evolved. In Proceedings of OZCHI 2003: New Directions in Interaction, Information Environments, Media & Technology. (pp. 34-43).
38. Spinellis, D. (2003). The decay and failures of web references, Communications of the ACM, 46(1):71-77.
39. Spinellis, D. (2005) Index-Based Persistent Document Identifiers. Information Retrieval, 8(1), 5-24.
40. Ullman, D. (2002). Toward the ideal mechanical engineering design support system. Research in Engineering Design, 13, 55-64.
41. Van Rijsbergen, C. J. (1979). Information retrieval, pp. 60. London: Butterworth.
42. Wang, J., Vries, A., & Reinders, M. (2006). A user-item relevance model for log-based collaborative filtering. In Proceedings of the European Conference on Information Retrieval (ECIR 2006). (pp. 37-48).
43. Wen, J., Nie, J., & Zhang, H. (2001). Query clustering using content words and user feedback. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval [(New Orleans, Louisiana, United States)]. (pp. 442-443). New York: NY. SIGIR '01. ACM Press.
44. White, R. W., Jose, J. M., & Ruthven, I. (2006). An implicit feedback approach for interactive information retrieval. Information Processing and Management, 42(1), 166-190.
45. Yang, Y. (1999). An evaluation of statistical approaches to text categorization. Information Retrieval, 1(1-2), 69-90.
46. Yang, Y., & Webb, G. I. (2001). Proportional k-Interval discretization for naive-bayes classifiers. In Proceedings of the 12th European Conference on Machine Learning. [(September 05-07, 2001)]. (pp. 564-575). Springer-Verlag: London.