Gondy Leroy
Skip Abstract Section
Abstract
Research has shown that most users' online information searches are suboptimal. Query optimization based on a relevance feedback or genetic algorithm using dynamic query contexts can help casual users search the Internet. These algorithms can draw on implicit user feedback based on the surrounding links and text in a search engine result set to expand user queries with a variable number of keywords in two manners. Positive expansion adds terms to a user's keywords with a Boolean "and," negative expansion adds terms to the user's keywords with a Boolean "not." Each algorithm was examined for three user groups, high, middle, and low achievers, who were classified according to their overall performance. The interactions of users with different levels of expertise with different expansion types or algorithms were evaluated. The genetic algorithm with negative expansion tripled recall and doubled precision for low achievers, but high achievers displayed an opposed trend and seemed to be hindered in this condition. The effect of other conditions was less substantial.
- Amati, G., Carpineto, C., and Romano, G. 2001. FUB at TREC-10 Web Track: A probabilistic framework for topic relevance term weighting. In Proceedings of the Tenth Text REtrieval Conference (TREC 2001, Gaithersburg, MD). 182--192.Google Scholar
- Attar, R. and Fraenkel, A. S. 1977. Local feedback in full-text retrieval systems. J. ACM 24, 3, 397--417. Google Scholar
- Belkin, N. J., Cool, C., Head, J., Jeng, J., Kelly, D., Lin, S., Lobash, L., Park, S. Y., Savage-Knepshield, P., and Sikora, C. 1999. Relevance feedback versus local context analysis as term suggestion devices: Rutgers' TREC-8 interactive track experience. In Proceedings of the Eighth Text REtrieval Conference (TREC 8, Gaithersburg, MD). 565--573.Google Scholar
- Bodner, R. C. and Chignell, M. H. 1998. ClickIR: Text retrieval using a dynamic hypertext interface. In Proceedings of the Seventh Text REtrieval Conference (TREC 7, Gaithersburg, MD). 573.Google Scholar
- Budzik, J. and Hammond, K. J. 2000. User interactions with everyday applications as context for just-in-time information access. In Proceedings of the 5th International Conference on Intelligent User Interfaces. 44--51. Google Scholar
- Chen, C. C., Chen, M. C., and Sun, Y. 2001. PVA. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 257--262. Google Scholar
- Chen, H., Chung, Y.-M., and Ramsey, M. 1998a. A smart itsy bitsy spider for the web. J. Amer. Soc. Inform. Sci. 49, 7, 604--618. Google Scholar
- Chen, H., Shankaranarayanan, G., and She, L. 1998b. A machine learning approach to inductive query by examples: An experiment using relevance feedback, ID3, genetic algorithms, and simulated annealing. J. Amer. Soc. Inform. Sci. 49, 8, 693--705. Google Scholar
- Claypool, M., Le, P., Wased, M., and Brown, D. 2001. Implicit interest indicators. In Proceedings of the International Conference on Intelligent User Interfaces (New York, NY). 33--40. Google Scholar
- De Lima, E. F. and Pedersen, J. O. 1999. Phrase recognition and expansion for short, precision-biased queries based on a query log. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Berkeley, CA). 145--152. Google Scholar
- Fan, W., Gordon, M. D., and Pathak, P. 2000. Personalization of search engine services for effective retrieval and knowledge management. In Proceedings of the International Conference on Information Systems (ICIS, Brisbane, Australia). 20--34. Google Scholar
- Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., and Ruppin, E. 2002. Placing search in context: The concept revisited. ACM Trans. Inform. Syst. 20, 1, 116--131. Google Scholar
- Fuller, R. and De Graaff, J. J. 1996. Measuring user motivation from server log files. In Proceedings of the Conference on Designing for the Web: Empirical Studies (Microsoft Campus).Google Scholar
- Harman, D. 1988. Towards interactive query expansion. In Proceedings of the Eleventh International Conference on Research & Development in Information Retrieval (New York, NY). 321--331. Google Scholar
- Harman, D. 1992. Relevance feedback revisited. In Proceedings of the 15th International ACM/SIGIR Conference on Research and Development in Information Retrieval. Google Scholar
- Hawking, D. and Craswell, N. 2001. Overview of the TREC-2001 Web Track (TREC 2001). In Proceedings of the Tenth Text REtrieval Conference (Gaithersburg, MD). 61--68.Google Scholar
- Hersh, W., Sacherek, L., and Olson, D. 2001. Observation of searchers: OHSU TREC 2001 interactive track. In Proceedings of the Tenth Text REtrieval Conference (TREC 2001, Gaithersburg, MD). 434. Google Scholar
- Hersh, W., Turpin, A., Price, S., Chan, B., Kramer, D., Sacherek, L., and Olson, D. 2000a. Do batch and user evaluations give the same results? In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Athens, Greece). Google Scholar
- Hersh, W., Turpin, A., Sacherek, L., Olson, D., Price, S., and Chan, B. 2000b. Further analysis of whether batch and user evaluations give the same results with a question-answering task. In Proceedings of the Ninth Text REtrieval Conference (TREC 9, Gaithersburg, MD, 407.Google Scholar
- Ide, E. 1971. New experiments in relevance feedback. In, The SMART Retrieval System: Experiments in Automatic Document Processing, G. Salton, Ed. Prentice-Hall, Englewood Cliffs, NJ, 337--354.Google Scholar
- Ide, E. and Salton, G. Interactive search strategies and dynamic file organization in information retrieval. In The SMART Retrieval System: Experiments in Automatic Document Processing, G. Salton, Ed. Prentice-Hall (1971), Englewood Cliffs, NJ, 373--393.Google Scholar
- Jansen, B., Spink, A., and Saracevic, T. 2000. Real life, real users, and real needs: A study and analysis of user queries on the web. Inform. Process. Manage. 36, 2, 207--227. Google Scholar
- Koenemann, J. and Belkin, N. J. 1996. A case for interaction: A study of interactive information retrieval behavior and effectiveness. In Proceedings of the Conference on Human Factors in Computing Systems (Vancouver, B.C., Canada). Google Scholar
- Kracker, J. and Wang, P. 2002. Research anxiety and students' perceptions of research: An experiment. Part II. Content analysis of their writings on two experiences. J. Amer. Soc. Inform. Sci. Tech. 53, 4, 295--307. Google Scholar
- Kraft, D. H., Petry, F. E., Buckles, B. P., and Sadasivan, T. 1994. The use of genetic programming to build queries for information retrieval. In Proceedings of the First IEEE Conference on Evolutionary Computation (New York, NY). 468--473.Google Scholar
- Lai, H. and Yang, T.-C. 2000. A system architecture for intelligent browsing on the Web. Decis. Supp. Syst. 28, 219--239.Google Scholar
- Magennis, M. and Rijsbergen, C. J. V. 1997. The potential and actual effectiveness of interactive query expansion. In Proceedings of the the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 342--332. Google Scholar
- Meyer, B., Sit, R. A., Spaulding, V. A., Mead, S. E., and Walker, N. 1997. Age group differences in Word Wide Web navigation. In Proceedings of the Conference on Human Factors in Computing Systems (Atlanta, GA). 295--296. Google Scholar
- Michalewicz, Z. 1992. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, New York, NY. Google Scholar
- Nick, Z. Z. and Themis, P. 2001. Web search using a genetic algorithm. IEEE Internet Comput. 5, 2, 18--26. Google Scholar
- Nordlie, R. 1999. User revealment---a comparison of initial queries and ensuing question development in online searching and in human reference interactions. In Proceedings of the Twenty-Second Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Berkeley, CA). 11--18. Google Scholar
- Pathak, P., Gordon, M., and Fan, W. 2000. Effective information retrieval using genetic algorithms based matching functions adaptation. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences. 533--540.Google Scholar
- Pitkow, J. E. and Kehoe, C. M. 1996. Emerging trends in the WWW user population. Commun. ACM 39, 6, 106--108. Google Scholar
- Robertson, S. E. and Sparck Jones, K. 1976. Relevance weighting of search terms. J. Amer. Soc. Inform. Sci. 27, 3, 129--146.Google Scholar
- Rocchio, J. J. Relevance feedback in information retrieval. In The SMART Retrieval System: Experiments in Automatic Document Processing, G. Salton, Ed. Prentice Hall, Englewood Cliffs, NJ, 313--323.Google Scholar
- Ross, N. C. M. and Wolfram, D. 2000. End user searching on the internet: An analysis of term pair topics submitted to the excite search engine. J. Amer. Soc. Inform. Sci. 51, 10, 949--958. Google Scholar
- Salton, G. and Buckley, C. 1990. Improving retrieval performance by relevance feedback. J. Amer. Soc. Inform. Sci. 41, 4, 288--297.Google Scholar
- Specht, M. and Kobsa, A. 1999. Interaction of domain expertise and interface design in adaptive educational hypermedia. In Proceedings of the Second Workshop on Adaptive Systems and User Modeling on the World Wide Web at the Eighth International World Wide Web Conference (Toronto, Ont., Canada). 89--93.Google Scholar
- Spink, A. 1996. Multiple search sessions model of end-user behavior: An exploratory study. J. Amer. Soc. Inform. Sci. 47, 8, 603--609. Google Scholar
- Spink, A., Wolfram, D., Jansen, M. B. J., and Saracevic, T. 2001. Searching the Web: The public and their queries. J. Amer. Soc. Inform. Sci. Tech. 52, 3, 226--234. Google Scholar
- Sullivan, D. 2000. NPD search and portal site study. Search Engine Watch: http://www.searchenginewatch.com/reports/npd.html.Google Scholar
- Thury, E. M. 1998. Analysis of student web browsing behavior: Implications for designing and evaluating Web sites. In Proceedings of the Sixteenth Annual International Conference on Computer Documentation (Quebec, Canada). 265--270. Google Scholar
- Toms, E. G., W. Kopak, R., Bartlett, J., and Freund, L. 2001. Selecting versus describing: A preliminary analysis of the efficacy of catgeories in exploring the Web. In Proceedings of the Tenth Text REtrieval Conference (TREC 2001, Gaithersburg, MD).Google Scholar
- Van Rijsbergen, C. J. 1979. Information Retrieval, 2nd ed. Butterworths, London, U. K. Google Scholar
- Vogt, C. C. 2000. Passive feedback collection---an attempt to debunk the myth of clickthroughs. In Proceedings of the Ninth Text REtrieval Conference (TREC 9, Gaithersburg, MD). 141.Google Scholar
- White, R. W., Jose, J. M., and Ruthven, I. 2001. Comparing explicit and implicit feedback techniques for Web retrieval: TREC-10 interactive track report. In Proceedings of the Tenth Text REtrieval Conference (TREC 2001, Gaithersburg, MD).Google Scholar
- White, R. W., Ruthven, I., and Jose, J. M. 2002. Finding relevant documents using top ranking sentences: An evaluation of two alternative schemes. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Finland). 57--64. Google Scholar
- Xu, J. and Croft, W. B. 1996. Query expansion using local and global document analysis. In Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 4--11. Google Scholar
- Xu, J. and Croft, W. B. 2000. Improving the effectiveness of information retrieval with local context analysis. ACM Trans. Inform. Syst. 18, 1, 79--112. Google Scholar
- Yang, J. J. and Korfhage, R. 1993. Query optimization in information retrieval using genetic algorithms. In Proceedings of the Fifth International Conference on Genetic Algorithms. 603--611. Google Scholar
- Yang, K. and Maglaughlin, K. 1999. IRIS at TREC-8. In Proceedings of the Eighth Text REtrieval Conference (TREC 8, Gaithersburg, MD). 645.Google Scholar
- Yang, K., Maglaughlin, K., Meho, L., and Sumner, R. G., Jr. 1998. IRIS at TREC-7. In Proceedings of the Seventh Text REtrieval Conference (TREC 7, Gaithersburg, MD). 555.Google Scholar
Index Terms
- The use of dynamic contexts to improve casual internet searching
Recommendations
Tuning of Expansion Terms by PRF and WordNet Integrated Approach for AQE
MIKE 2013: Proceedings of the First International Conference on Mining Intelligence and Knowledge Exploration - Volume 8284Vocabulary mismatch in Information retrieval can be solved by Query Expansion (QE) techniques. Relevance feedback is a prominent solution to improve recall of retrieval system. Sometimes user may be reluctant and novice in providing feedback to improve ...
"A term is known by the company it keeps": On Selecting a Good Expansion Set in Pseudo-Relevance Feedback
ICTIR '09: Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval TheoryIt is well known that pseudo-relevance feedback (PRF) improves the retrieval performance of Information Retrieval (IR) systems in general. However, a recent study by Cao et al [3] has shown that a non-negligible fraction of expansion terms used by PRF ...
Arabic Query Expansion Using WordNet and Association Rules
Query expansion is the process of adding additional relevant terms to the original queries to improve the performance of information retrieval systems. However, previous studies showed that automatic query expansion using WordNet do not lead to an ...
Comments