A Query Dependent Approach to Learning to Rank for Information Retrieval

This paper proposes a new ranking approach for information retrieval, where the diversity among queries are taken into consideration. In information retrieval, the users' queries often vary a lot from one to another, so that the documents retrieved from different queries are also distributed differently. Due to this diversity, it is not appropriate to assume all the documents to be ranked are generated i.i.d. (independently and identically distributed) according to a fixed but unknown probability distribution. However, most of the existing learning to rank approaches are proposed on the basis of the conventional i.i.d. assumption. In this paper, the conventional i.i.d. assumption is relaxed to fit the real situations of information retrieval better, and then a new ranking approach, referred to as 'query dependent ranking', is proposed. In our approach, the ranking models for different queries have generality while each of them has its own speciality. The experimental results on both synthetic and real-world datasets show the advantage of our approach to conventional ranking approaches.

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