Travis L. Bauer
David B. Leake
Abstract
Personal information agents monitor ongoing user information accesses in order to provide users with context-relevant information. Providing the needed information requires effective methods for identifying the user's task context, based on available information. For user browsing tasks, one approach to context identification is to extract context-determining terms from the documents that the user consults. The thesis of this article is (1) that term extraction for personal information agents can be done by learning terms whose occurrence frequencies have a large variance over time, (2) that indexing and retrieval based on these terms can be at least as effective as standard information retrieval techniques, and (3) that this information can be learned without comprehensive corpus analysis, making it suitable for use in personal information retrieval.We have developed an unsupervised term extraction algorithm, WordSieve, that learns individualized context-differentiating terms for document indexing and retrieval. This article presents a new version of WordSieve, compares its design and performance to our initial approach, and assesses its effectiveness for a controlled personal information retrieval task, compared to three common indexing techniques requiring statistics about the global corpus. In the experiments, the new version of WordSieve generates task-relevant indices of comparable or better quality to common indexing techniques, using only local information.
- Gediminas Adomavicius and Alexander Tuzhilin. Using data mining methods to build customer profiles. Computer, 34(2):74--82, February 2001.]] Google Scholar
Digital Library
- Ricardo Baeza-Yates and Berthier Ribeiro-Neto. Modern Information Retrieval. ACM Press, 1999.]] Google ScholarDigital Library
- Marko Balabanović and Yoav Shoham. Learning information retrieval agents: Experiments with automated web browsing. In Proceedings of the AAAI Spring Symposium on Information Gathering from Heterogeneous, Distributed-Resources, March 1995.]]Google Scholar
- Travis Bauer and David Leake. Real time user context modeling for information retrieval agents. In Tenth International Conference on Information and Knowledge Management, pages 568--570. ACM Press, 2001.]] Google ScholarDigital Library
- Travis Bauer and David Leake. Wordsieve: A method for real-time context extraction. In Modeling and Using Context: Proceedings of the Third International and Interdisciplinary Conference, Context 2001, pages 30--44.Springer-Verlag, 2001.]] Google ScholarDigital Library
- Travis Bauer and David Leake. Calvin: A multi-agent persoanl information retrieval system. In Agent Oriented Information Systems 2002: Proceedings of the Fourth International Bi-Conference Workshop, 2002.]]Google Scholar
- Travis Bauer and David Leake. Using document access sequences to recommend customized information. IEEE Intelligent Systems, 17(6):27--32, Nov/Dec 2002.]] Google ScholarDigital Library
- J. Budzik, K. Hammond, and L. Birnbaum. Information access in context. In Knowledge based systems, 2001.]]Google Scholar
- T. Joachims, D. Freitag, and T. Mitchell. Webwatcher: A tour guide for the world wide web. In Proceedings of IJCA197, August 1997.]]Google Scholar
- David B. Leake and Ryan Scherle. Towards context-based search engine selection. In Proceedings on the International Conference on Intelligent User Interfaces, pages 109--112, Santa Fe, NM, Jan 2001.]] Google ScholarDigital Library
- Seng Wai Loke, Andrew Davison, and Leon Sterling. CIFI: An intelligent agent for citation finding on the world-wide web. In Pacific Rim International Conference on Artificial Intelligence, pages 580--591, 1996.]] Google ScholarDigital Library
- U. Manber, M. Smith, and B. Gopal. Webglimpse: Combining browsing and searching. In Proceedings of 1997 Usenix Technical Conference, 1997.]] Google ScholarDigital Library
- M. Pazzani, J. Muramatsu, and D. Billsus. Syskill & webert: Identifying interesting web sites. In Proceedings of the National Conference on Artificial Intelligence, Portland, OR, 1996.]]Google ScholarDigital Library
- M. Pazzani, L. Nguyen, and S. Mantik. Learning from hotlists and coldlists: towards a www information filtering and seeking agent. In Proceedings of AI Tools Conference, Washington, DC, 1995.]] Google ScholarDigital Library
- Murray R. Spiegel. Mathematical Handbook of Formulas and Tables. Shaum's Outline Series in Mathematics. McGraw-Hill Book Company, 1968.]]Google Scholar
- Ahmad M. Ahmad Wasfi. Collecting user access patterns for building user profiles and collaborative filtering. In Proceedings of the 4th international conference on Intelligent user interfaces, pages 57--64. ACM Press, 1999.]] Google ScholarDigital Library
Index Terms
- Detecting context-differentiating terms using competitive learning
Recommendations
Detecting similar documents using salient terms
CIKM '02: Proceedings of the eleventh international conference on Information and knowledge managementWe describe a system for rapidly determining document similarity among a set of documents obtained from an information retrieval (IR) system. We obtain a ranked list of the most important terms in each document using a rapid phrase recognizer system. We ...
Chinese information retrieval based on terms and relevant terms
In this article we describe our approach to Chinese information retrieval, where a query is a short natural language description. First, we use automatically extracted short terms from document sets to build indexes and use the short terms in both the ...
Improving retrieval effectiveness by using key terms in top retrieved documents
ECIR'05: Proceedings of the 27th European conference on Advances in Information Retrieval ResearchIn this paper, we propose a method to improve the precision of top retrieved documents in Chinese information retrieval where the query is a short description by re-ordering retrieved documents in the initial retrieval. To re-order the documents, we ...
Comments