Greedy feature selection for ranking

This paper is concerned with a study on the feature selection for ranking. Learning to rank is a useful tool for collaborative filtering and many other collaborative systems, which many algorithms have been proposed for dealing this issue. But feature selection methods receive little attention, despite of their importance in collaborative filtering problems: First, recommender systems always have massive data. Using all these data in learning to rank is unrealistic and impossible. Second, we discuss that not all the features are useful for a user's query. So choosing the most relevant data is necessary and useful. To amend this problem, we describe an algorithm called FBPCRank to choose the most relevant features for ranking. Our method combines two measures of good subsets of features, which not only can decrease the loss objective, but also reduce total similarity scores between any two features. We adopt forward and backward methods to choose the most relative features and use Pearson correlation coefficient to measure the similarity of two features. The experiments indicate that our method can outperform other state-of-the-art algorithms by selecting just small amounts of features.