comparing relevance feedback algorithms for web search
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.
relevance relevance feedback algorithm web search evaluation context use experimentation user documentation effect computation performance variation test