Low-Complexity Supervised Rank Fusion Models
André Mourão, João Magalhães · 2018
Combining multiple retrieval functions can lead to notable gains in retrieval performance. Learning to Rank (LETOR) techniques achieve outstanding retrieval results, by learning models with no bounds on model complexity. Often, minor retrieval gains are attained at a significant cost in model complexity. This paper focuses on the research question:can less complex models achieve results comparable to LETOR models? In this paper, we investigate an approach for the selection and fusion of rank lists with low-complexity models. The described Learning to Fuse (L2F) algorithm, is a supervised rank fusion procedure that controls the model complexity by discarding rank lists that bring minor improvements to final rank. Evaluation results, on two different datasets, show that it is indeed possible to achieve a retrieval performance comparable to LETOR methods, using only 3-5% of the rank lists of the number of rank lists used by LETOR methods.