An incremental approach to efficient pseudo-relevance feedback

Abstract

Pseudo-relevance feedback is an important strategy to improve search accuracy. It is often implemented as a two-round retrieval process: the first round is to retrieve an initial set of documents relevant to an original query, and the second round is to retrieve final retrieval results using the original query expanded with terms selected from the previously retrieved documents. This two-round retrieval process is clearly time consuming, which could arguably be one of main reasons that hinder the wide adaptation of the pseudo-relevance feedback methods in real-world IR systems. In this paper, we study how to improve the efficiency of pseudo-relevance feedback methods. The basic idea is to reduce the time needed for the second round of retrieval by leveraging the query processing results of the first round. Specifically, instead of processing the expand query as a newly submitted query, we propose an incremental approach, which resumes the query processing results (i.e. document accumulators) for the first round of retrieval and process the second round of retrieval mainly as a step of adjusting the scores in the accumulators. Experimental results on TREC Terabyte collections show that the proposed incremental approach can improve the efficiency of pseudo-relevance feedback methods by a factor of two without sacrificing their effectiveness.