A Graph-based Feature Selection Method for Learning to Rank Using Spectral Clustering for Redundancy Minimization and Biased PageRank for Relevance Analysis

Jen-Yuan Yeh, Cheng-Jung Tsai

This paper addresses the feature selection problem in learning to rank (LTR). We propose a graph-based feature selection method, named FS-SCPR, which comprises four steps: (i) use ranking information to assess the similarity between features and construct an undirected feature similarity graph; (ii) apply spectral clustering to cluster features using eigenvectors of matrices extracted from the graph; (iii) utilize biased PageRank to assign a relevance score with respect to the ranking problem to each feature by incorporating each feature's ranking performance as preference to bias the PageRank computation; and (iv) apply optimization to select the feature from each cluster with both the highest relevance score and most information of the features in the cluster. We also develop a new LTR for information retrieval (IR) approach that first exploits FS-SCPR as a preprocessor to determine discriminative and useful features and then employs Ranking SVM to derive a ranking model with the selected features. An evaluation, conducted using the LETOR benchmark datasets, demonstrated the competitive performance of our approach compared to representative feature selection methods and state-of-the-art LTR methods.