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Exploiting information access patterns for context-based retrieval

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 contextbased 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 evaluati.