Abstract
We present a novel approach to re-ranking a document list that was retrieved in response to a query 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 a long-standing challenge, namely, integration of document-based and cluster-based retrieved results.
Original language | English |
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Pages (from-to) | 413-437 |
Number of pages | 25 |
Journal | Information Retrieval |
Volume | 14 |
Issue number | 4 |
DOIs | |
State | Published - Aug 2011 |
Keywords
- Ad hoc retrieval
- Cluster-based retrieval
- Inter-document-similarities
- Re-ranking
- Similarity-based fusion
All Science Journal Classification (ASJC) codes
- Information Systems
- Library and Information Sciences