Multiple query-dependent RankSVM aggregation for document retrieval

This paper is concerned with supervised rank aggregation, which aims to improve the ranking performance by combining the outputs from multiple rankers. However, there are two main shortcomings in previous rank aggregation approaches. Firstly, the learned weights for base rankers do not distinguish the differences among queries. This is suboptimal since queries vary significantly in terms of ranking. Besides, most current aggregation functions are unsupervised. A supervised aggregation function could further improve the ranking performance. In this paper, the significant difference existing among queries is taken into consideration, and a supervised rank aggregation approach is proposed. As a case study, we employ RankSVM model to aggregate the base rankers, referred to as Q.D.RSVM, and prove that Q.D.RSVM can set up query-dependent weights for different base rankers. Experimental results based on benchmark datasets show our approach outperforms conventional ranking approaches.