Collecting user access patterns for building user profiles and collaborative filtering
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
Towards context-based search engine selection
Proceedings of the 6th international conference on Intelligent user interfaces
Real time user context modeling for information retrieval agents
Proceedings of the tenth international conference on Information and knowledge management
Modern Information Retrieval
Using Document Access Sequences to Recommend Customized Information
IEEE Intelligent Systems
CIFI: An Intelligent Agent for Citation Finding on The World-wide Web
PRICAI '96 Proceedings of the 4th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Learning from Hotlists and Coldlists: Towards a WWW Information Filtering and Seeking Agent
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
WebGlimpse: combining browsing and searching
ATEC '97 Proceedings of the annual conference on USENIX Annual Technical Conference
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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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.