Learning to Rank Query Reformulations

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.