Evaluating implicit measures to improve Web search

ACM Transactions on Information Systems

Volumn 23, Issue 2, 2005, Pages 147-168

  Fox, Steve ^a   Karnawat, Kuldeep ^a   Mydland, Mark ^a   Dumais, Susan ^a   White, Thomas ^a  

^a MICROSOFT   (United States)

Author keywords

Explicit feedback; Explicit ratings; Implicit measures; Prediction model; Search sessions; User interest; User satisfaction

Indexed keywords

EXPLICIT FEEDBACK; EXPLICIT RATINGS; IMPLICIT MEASURES; PREDICTION MODEL; SEARCH SESSIONS; USER INTEREST; USER SATISFACTION;

DATA ACQUISITION; FEEDBACK; MATHEMATICAL MODELS; ONLINE SEARCHING; USER INTERFACES; WORLD WIDE WEB;

SEARCH ENGINES;

EID: 20344382508     PISSN: 10468188     EISSN: None     Source Type: Journal    
DOI: 10.1145/1059981.1059982     Document Type: Article

References (17)

1. CHICKERING, D. M. 2002. The WinMine Tookit. Microsoft Research Tech. Rep. MSR-TR-2002-102. Microsoft Research, Redmond, WA. Go online to http://research.microsoft.com/~dmax/WinMine/tooldoc.htm.
2. CHICKERING, D. M., HECKERMAN, D., AND MEEK, C. 1997. A Bayesian approach to learning bayesian networks with local structure. In Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence. 80-89.
3. CLAYPOOL, M., BROWN, D., LE, P., AND WASEDA, M. 2001. Inferring user interest. IEEE Internet Comput. 5, 6 (Nov.-Dec.), 32-39.
4. COOPER, G. AND HERSKOVITS, E. 1992. A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9, 309-347.
5. GOECKS, J. AND SHAVLIK, J. 1999. Learning users' interests by unobtrusively observing their normal behavior. In Proceedings of the IJCAI Workshop on Machine Learning for Information Filtering. 129-132.
6. HECKERMAN, D., GEIGER, D., AND CHICKERING, D. M. 1995. Learning Bayesian networks: The combination of knowledge and statistical data. Mach. Learn. 20. 197-243.
7. HORVITZ, E., BREESE, J., HECKERMAN, D., HOVEL, D., AND ROMMELSE, K. 1998. The Lumiere Project: Bayesian user modeling for inferring the goals and needs of software users. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (July). 256-265.
8. JOACHIMS, T. 2002. Optimizing search engines using clickthrough data. In Proceedings of KDD 2004. 133-142.
9. KELLY, D. AND TEEVAN, J. 2003. Implicit feedback for inferring user preference: A bibliography. SIGIR For. 37, 2, 18-28.
10. KONSTAN, J., MILLER, B., MALTZ, D., HERLOCKER, J., GORDON, L., AND RIEDL, J. 1997. GroupLens: Applying collaborative filtering to usenet news. Commun. ACM 40, 3, 77-87.
11. MORITA, M. AND SHINODA, Y. 1994. Information filtering based on user behavior analysis and best match text retrieval. In Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval (July). 272-281.
12. NICHOLS, D. M. 1997. Implicit ratings and filtering. In Proceedings of the Fifth DELOS Workshop on Filtering and Collaborative Filtering (Nov.). 221-228.
13. OARD, D. AND KIM, J. 1998. Implicit feedback for recommender systems. In Proceedings of the AAAI Workshop on Recommender Systems (July). 81-83.
14. OARD, D. W. AND KIM, J. 2001. Modeling information content using observable behavior. In Proceedings of the 64th Annual Meeting of the American Society for Information Science and Technology. 38-45.
15. SILVERSTEIN, C., HENZINGER, M., MARAIS, H., AND MORICZ, M. 1998. Analysis of a very large AltaVista query log. SRC Tech. Note 1998-014, Compaq Systems Research Center, Palo Alto, CA. Website: http://www.research.Compaq.com/SRC/ publications.
16. SPINK, A., WOLFRAM, D., JANSEN, B. J., AND SARACEVIC, T. 2001. Searching the Web: The public and their queries: J. Amer. Soci. Informat. Sci. 52, 3, 226-234.
17. VOORHEES, E. 2001. Evaluation by highly relevant documents. In Proceedings of the Twenty-Fourth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval. 74-82.