From “Identical” to “Similar”: Fusing Retrieved Lists Based on Inter-document Similarities

Abstract

Methods for fusing document lists that were retrieved in response to a query often utilize the retrieval scores and/or ranks of documents in the lists. We present a novel fusion approach that is based on using, in addition, information induced from inter-document similarities. Specifically, our methods let similar documents from different lists provide relevance-status support to each other. We use a graph-based method to model relevancestatus propagation between documents. The propagation is governed by inter-documentsimilarities and by retrieval scores of documents in the lists. Empirical evaluation demonstrates the effectiveness of our methods in fusing TREC runs. The performance of our most effective methods transcends that of effective fusion methods that utilize only retrieval scores or ranks.1. We use the term "overlap" to refer to the number of documents shared by the retrieved lists rather than in reference to content overlap.

Re-ranking search results using an additional retrieved list

“…The second important difference between the work in Kozorovitzky and Kurland (2009) and our methods lies in the actual techniques used for exploiting inter-document similarities. One of the strengths and key underlying ideas of the fusion method in Kozorovitzky and Kurland (2009) is the use of PageRank (Brin and Page 1998) so as to utilize a recursive definition of which documents are central to the entire set of fused lists.…”

“…Recent work (Kozorovitzky and Kurland 2009) utilizes inter-document similarities for fusing a few retrieved lists. Documents that are ranked high in many of the lists and that are similar to many other documents that are ranked high in many of the lists, end at high ranks of the final list.…”

“…There are two important differences between this work and ours. The first is that while the proposed method in Kozorovitzky and Kurland (2009) is a symmetric fusion method in that all lists to be fused are of the same importance, our methods could be viewed as asymmetric fusion techniques as they re-rank a list based on inter-document similarities with a second list. In fact, due to the nature of the approaches, it is not clear how to naturally transform the method in Kozorovitzky and Kurland (2009) to an asymmetric technique for re-ranking, or alternatively, transform our methods into symmetric fusion approaches.…”

“…One of the strengths and key underlying ideas of the fusion method in Kozorovitzky and Kurland (2009) is the use of PageRank (Brin and Page 1998) so as to utilize a recursive definition of which documents are central to the entire set of fused lists. Our re-ranking methods, on the other hand, could be viewed as looking for central documents within L init by using a simple weighted in-degree criterion employed over a oneway bipartite graph wherein documents in L init are on one side, and documents in L help are on the other side; edges point from the latter list to the former.…”

From fusion to re-ranking

“…Although the usage of this kind of strategy showed at times slight improvements, in most cases they are not significant. This paper presents a different approach to document aggregation based on a variation of the "inter-document similarities" [8,10] idea. We combine two retrieval strategies that work at two different representation levels: keyword and synset.…”