Aggregation on Learning to Rank for Consumer Health Information Retrieval

Abstract

Common people are increasingly acquiring health information depending on general search engines which are still far from being effective in dealing with complex consumer health queries. One prime and effective method in addressing this problem is using Learning to Rank (L2R) techniques. In this paper, an investigation on aggregation over field-based L2R models is made. Rather than combining all potential features into one list to train a L2R model, we propose to train a set of L2R models each using features extracted from only one field and then apply aggregation methods to combine the results obtained from each model. Extensive experimental comparisons with the state-of-the-art baselines on the considered data collections confirmed the effectiveness of our proposed approach.