Abstract
Contextual retrieval supports differences amongst users in their information seeking requests. The Web, which is very dynamic and nearly universally accessible, is an environment in which it is increasingly difficult for users to find documents that satisfy their specific information needs. This problem is amplified as users tend to use short queries. Contextual retrieval attempts to address this problem by incorporating knowledge about the user and past retrieval results in the search process. In this paper we explore a feedback technique based on the Rocchio algorithm that significantly reduces demands on the user while maintaining comparable performance on the Reuters-21578 corpus.
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References
Allan, J.: Incremental Relevance Feedback for Information Filtering. In: Proc. of the 19th An-nual Int. ACM SIGIR Conf. on Research and Development in Info. Retri., pp. 270–278 (1996)
Arasu, A., Cho, J., Garcia-Molina, H., Paepcke, A., Raghavan, S.: Searching the Web. ACM Transactions on Internet Technology 1(1), 2–43 (2001)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison Wesley, Reading (1999)
Belkin, N.J., Cool, C., Kelly, D., Kim, G., Kim, J.-Y., Lee, H.-J., Muresan, G., Tang, M.-C., Yuan, X.-J.: Query Length in Interactive Information Retrieval. In: Proc. of the 26th Annual Int. ACM SIGIR Conf. on Research and Development in Info. Retri., 205–212 (2003)
Billsus, D., Pazzani, M.: A hybrid user model for news story classification. In: Proceedings of the Seventh International Conference on User Modeling, pp. 99–108 (1999)
Chen, L., Sycara, K.: WebMate: A Personal Agent for Browsing and Searching. In: Proceedings of the Second International Conference on Autonomous Agents, pp. 132–139 (1998)
Chan, P.K.: Constructing Web User Profiles: A non-invasive Learning Approach. In: Revised Papers from the Int. Workshop on Web Usage Analysis and User Profiling, pp. 39–55 (1999)
Joachims, T.: A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. In: Proceedings of ICML 1997, pp. 143–146 (1997)
Kobsa, A.: User modeling: Recent work, prospects and hazards. In: Schneider-Huchmidt, M., Kiihme, T., Malinowski, U. (eds.) Adaptive User Interfaces: Principles and Practice, pp. 111–128 (1993)
Koychev, I., Schwab, I.: Adaptation to drifting user’s interests. In: Proceedings of ECML 2000/MLnet Workshop: Machine Learning in New Information Age, pp. 39–46 (2000)
Kruschwitz, U.: An Adaptable Search System for Collections of Partially Structured Documents. IEEE Intelligent Systems, 44–52 (July/August 2003)
Lawrence, S.: Context in Web Search. IEEE Data Engineering Bulletin 23(3), 25–32 (2000)
Liu, F., Yu, C., Meng, W.: Personalized web search by mapping user queries to categories. In: Proc. of the 11 Int. Conf. on Information and knowledge management (2002)
Middleton, S., DeRoure, D., Shadbolt, N.: Capturing knowledge of user preferences: Ontologies in recommender systems. In: Proc. of the 1st Int. Conf. on Knowledge Capture (2001)
Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conference on Digital Libraries, pp. 195–204 (2000)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An open architecture for collaborative filtering of Netnews. In: Proc. of ACM Conf. on Computer-Supported Cooperative Work, pp. 175–186 (1994)
Schapire, R., Singer, Y., Singhal, A.: Boosting and Rocchio applied to text filtering. In: Proc. of the 11th Int. Conf. on Research and Development in Information Retrieval, pp. 215–223 (1998)
Sieg, A., Mobasher, B., Lytinen, S., Burke, R.: Concept based query enhancement in ARCH. In: To appear in Proc. of the Int. Conf. on Internet Computing (2003)
Spink, A., Ozmutlu, H.C., Ozmutlu, S.: Multitasking information seeking and searching processes. Journal of the American Society for Information Science and Technology 53(8), 639–652 (2002)
Widyantoro, D.H., Ioerger, T.R., Yen, J.: An Adaptive Algorithm for Learning Changes in User Interests. In: Proc. of the 8th Int. Conf. on Information and Knowledge Management, pp. 405–412 (1999)
Glasgow IDOM - IR linguistic utilities: Stop word list, Access: October 24 (2003), http://www.dcs.gla.ac.uk/idom/ir_resources/linguistic_utils/
UCI KDD Archive: Reuters-21578 Text Categorization Collection, Access: October 24 (2003), http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html
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Jordan, C., Watters, C. (2004). Extending the Rocchio Relevance Feedback Algorithm to Provide Contextual Retrieval. In: Favela, J., Menasalvas, E., Chávez, E. (eds) Advances in Web Intelligence. AWIC 2004. Lecture Notes in Computer Science(), vol 3034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24681-7_16
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DOI: https://doi.org/10.1007/978-3-540-24681-7_16
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