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FEATURES: Real‐time adaptive feature and document learning for web search

Author

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  • Zhixiang Chen
  • Xiannong Meng
  • Richard H. Fowler
  • Binhai Zhu

Abstract

In this article we report our research on building FEATURES—an intelligent web search engine that is able to perform real‐time adaptive feature (i.e., keyword) and document learning. Not only does FEATURES learn from the user's document relevance feedback, but it also automatically extracts and suggests indexing keywords relevant to a search query and learns from the user's keyword relevance feedback so that it is able to speed up its search process and to enhance its search performance. We design two efficient and mutual‐benefiting learning algorithms that work concurrently, one for feature learning and the other for document learning. FEATURES employs these algorithms together with an internal index database and a real‐time meta‐searcher to perform adaptive real‐time learning to find desired documents with as little relevance feedback from the user as possible. The architecture and performance of FEATURES are also discussed.

Suggested Citation

  • Zhixiang Chen & Xiannong Meng & Richard H. Fowler & Binhai Zhu, 2001. "FEATURES: Real‐time adaptive feature and document learning for web search," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 52(8), pages 655-665.
    • Handle: RePEc:bla:jamist:v:52:y:2001:i:8:p:655-665
    DOI: 10.1002/asi.1115
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    Cited by:

    1. Zhixiang Chen & Bin Fu & John Abraham, 2010. "A quadratic lower bound for Rocchio’s similarity-based relevance feedback algorithm with a fixed query updating factor," Journal of Combinatorial Optimization, Springer, vol. 19(2), pages 134-157, February.

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