|M.Sc Student||Anna Kozorovitsky|
|Subject||Fusing Retrieved Lists Based on Inter-Document Similarities|
|Department||Department of Industrial Engineering and Management||Supervisor||Full Professor Kurland Oren|
|Full Thesis text|
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 similar- ities. Specifically, our methods let similar documents from different lists provide relevance-status support to each other. We use a graph-based method to model relevance-status propagation between documents. The propagation is governed by inter-document-similarities 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.