Lior Meister

We present a novel approach to re-ranking a document list that was retrieved in response to a query by some search algorithm so as to improve precision at the very top ranks. The approach is based on utilizing a second list that was retrieved in response to the query by using, for example, a different retrieval method and/or query representation. In contrast to commonly-used methods for fusion of retrieved lists that rely solely on retrieval scores (ranks) of documents, our approach also exploits inter-document-similarities between the lists --- a potentially rich source of additional information. Empirical evaluation shows that our methods are effective in re-ranking TREC runs; the resultant performance also favorably compares with that of a highly effective fusion method. Furthermore, we show that our methods can potentially help to tackle two long-standing challenges; namely, integration of document-based and cluster-based retrieved results; and, improvement of the performance robustness, and overall effectiveness, of pseudo-feedback-based retrieval.