Comparing relevance feedback algorithms for web search

Authors:
Vishwa Vinay;Ken Wood;Natasa Milic-Frayling;Ingemar J. Cox
Affiliations:
University College London, London, UK;Microsoft Research Ltd., Cambridge, UK;Microsoft Research Ltd., Cambridge, UK;University College London, London, UK
Venue:
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Year:
2005

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Abstract

We evaluate three different relevance feedback (RF)algorithms, Rocchio, Robertson/Sparck-Jones (RSJ)and Bayesian, in the context of Web search. We use a target-testing experimental procedure whereby a user must locate a specific document. For user relevance feedback, we consider all possible user choices of indicating zero or more relevant documents from a set of 10 displayed documents. Examination of the effects of each user choice permits us to compute an upper-bound on the performance of each RF algorithm.We ind that there is a significant variation in the upper-bound performance o the three RF algorithms and that the Bayesian algorithm approaches the best possible.