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ABSTRACT

Search systems have for some time provided users with the ability to request documents similar to a given document. Interfaces provide this feature via a link or button for each document in the search results. We call this feature find-similar or similarity browsing. We examined find-similar as a search tool, like relevance feedback, for improving retrieval performance. Our investigation focused on find-similar's document-to-document similarity, the reexamination of documents during a search, and the user's browsing pattern. Find-similar with a query-biased similarity, avoiding the reexamination of documents, and a breadth-like browsing pattern achieved a 23% increase in the arithmetic mean average precision and a 66% increase in the geometric mean average precision over our baseline retrieval. This performance matched that of a more traditionally styled iterative relevance feedback technique.

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INDEX TERMS

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

General Terms:
Algorithms, Experimentation

Keywords:
find-similar, relevance feedback, similarity browsing