Exploiting information access patterns for context-based retrieval

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 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.

REFERENCES

• 1: Jeremy Goecks , Jude Shavlik, Learning users' interests by unobtrusively observing their normal behavior, Proceedings of the 5th international conference on Intelligent user interfaces, p.129-132, January 09-12, 2000, New Orleans, Louisiana, United States [doi>10.1145/325737.325806]
• 2: Michael W. Berry , Susan T. Dumais , Todd A. Letsche, Computational Methods for Intelligent Information Access, Proceedings of the 1995 ACM/IEEE conference on Supercomputing (CDROM), p.20, December 04-08, 1995, San Diego, California, United States [doi>10.1145/224170.285569]
• 3: Travis Bauer , David B. Leake, WordSieve: A Method for Real-Time Context Extraction, Proceedings of the Third International and Interdisciplinary Conference on Modeling and Using Context, p.30-44, July 27-30, 2001
• 4: Leake, David, Travis Bauer, Anna Maguitman, and David Wilson. Capture, Storage and Reuse of Lessons about Information Resources: Supporting Task-Based Information Search, in Proceedings of the AAAI-2000 Workshop on Intelligent Lessons Learned Systems, AAAI Press, pp. 33-37.
• 5: Budzik, J., K. Hammond, and L. Birnbaum. Information access in context. Knowledge based systems. 14:37-53, 2001
• 6: Henry Lieberman. Letizia: An Agent That Assists Web Browsing. In Proceedings of IJCAI-95, Morgan Kaufmann, pp. 924-929.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.2 Design Tools and Techniques
          Subjects: User interfaces

  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.2 Design Methodology
          Subjects: Pattern analysis

General Terms:
Design, Human Factors, Performance, Theory

Keywords:
information retrieval, recommender systems