Learning to rank query reformulations

Authors:
Van Dang;Michael Bendersky;W. Bruce Croft
Affiliations:
University of Massachusetts, Amherst, MA, USA;University of Massachusetts, Amherst, MA, USA;University of Massachusetts, Amherst, MA, USA
Venue:
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Year:
2010

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Abstract

Query reformulation techniques based on query logs have recently proven to be effective for web queries. However, when initial queries have reasonably good quality, these techniques are often not reliable enough to identify the helpful reformulations among the suggested queries. In this paper, we show that we can use as few as two features to rerank a list of reformulated queries, or expanded queries to be specific, generated by a log-based query reformulation technique. Our results across five TREC collections suggest that there are consistently more useful reformulations in the first two positions in the new ranked list than there were initially, which leads to statistically significant improvements in retrieval effectiveness.