Utilizing relevance feedback in fusion-based retrieval

Abstract

Work on using relevance feedback for retrieval has focused on the single retrieved list setting. That is, an initial document list is retrieved in response to the query and feedback for the most highly ranked documents is used to perform a second search. We address a setting wherein the list for which feedback is provided results from fusing several intermediate retrieved lists. Accordingly, we devise methods that utilize the feedback while exploiting the special characteristics of the fusion setting. Specifically, the feedback serves two different, yet complementary, purposes. The first is to directly rank the pool of documents in the intermediate lists. The second is to estimate the effectiveness of the intermediate lists for improved re-fusion. In addition, we present a meta fusion method that uses the feedback for these two purposes simultaneously. Empirical evaluation demonstrates the merits of our approach. As a case in point, the retrieval performance is substantially better than that of using the relevance feedback as in the single list setting. The performance also substantially transcends that of a previously proposed approach to utilizing relevance feedback in fusion-based retrieval. Copyright 2014 ACM.