Exploiting Information Access Patterns for Context-Based Retrieval
(pdf
)
Bauer, T. and Leake, D., Exploiting Information Access Patterns for
Context-Based Retrieval. Proceedings of the 2002 International
Conference on Intelligent User Interfaces, ACM Press, 2002,
pp. 176-177.
Abstract
In order for intelligent interfaces to provide proactive assistance, they
must customize their behavior based on the user's task context. Existing
systems often assess context based on a single snapshot of the user's
current activities (e.g., examining the content of the document that the
user is currently consulting). However, an accurate picture of the user's
context may depend not only on this local information, but also on
information about the user's behavior over time. This paper discusses work
on a recommender system, Calvin, which learns to identify broader contexts
by relating documents that tend to be accessed together. Calvin's text
analysis algorithm, WordSieve, develops term vector descriptions of these
contexts in real time, without needing to accumulate comprehensive
statistics about an entire corpus. Calvin uses these descriptions (1) to
index documents to suggest them in similar future contexts and (2) to
formulate context-based queries for search engines. Results of initial
experiments are encouraging for the approach's improved ability to
associate documents with the research tasks in which they were consulted,
compared to methods using only local information. This paper sketches the
project goals, the current implementation of the system, and plans for its
continued development and evaluation.
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