Exploiting Result Diversification Methods for Feature Selection in Learning to Rank.

In this paper, we adopt various greedy result diversification strategies to the problem of feature selection for learning to rank. Our experimental evaluations using several standard datasets reveal that such diversification methods are quite effective in identifying the feature subsets in comparison to the baselines from the literature.