Abstract：Query Expansion is an important technique for improving retrieval performance. It uses some strategies to add some relevant terms to the original query submitted by the user, which could express the user’s information need more exactly and completely. Learning to rank is a hot machine learning issue addressed in in information retrieval, seeking to automatically construct ranking models determining the relevance degrees between objects. This paper attempts to improve pseudo-relevance feedback by introducing learning to rank algorithm to re-rank expansion terms. Some term features are obtained from the original query terms and the expansion terms, learning from which we can get a new ranking list of expansion terms. Adding the expansion terms list to the original query, we can acquire more relevant documents and improve the rate of accuracy. Experimental results on the TREC dataset shows that incorporating ranking algorithms in query expansion can lead to better retrieval performance.
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