Journal
ACM TRANSACTIONS ON INFORMATION SYSTEMS
Volume 37, Issue 2, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3300196
Keywords
Learning to rank; feature selection; risk-sensitiveness
Categories
Funding
- project InWeb [MCT/CNPq 573871/2008-6]
- project MASWeb [FAPEMIG/PRONEX APQ-01400-14]
- CNPq
- CAPES
- FAPEMIG
- FAPEGO
- IFG
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Learning to Rank (L2R) is one of the main research lines in Information Retrieval. Risk-sensitive L2R is a subarea of L2R that tries to learn models that are good on average while at the same time reducing the risk of performing poorly in a few but important queries (e.g., medical or legal queries). One way of reducing risk in learned models is by selecting and removing noisy, redundant features, or features that promote some queries to the detriment of others. This is exacerbated by learning methods that usually maximize an average metric (e.g., mean average precision (MAP) or Normalized Discounted Cumulative Gain (NDCG)). However, historically, feature selection (FS) methods have focused only on effectiveness and feature reduction as the main objectives. Accordingly, in this work, we propose to evaluate FS for L2R with an additional objective in mind, namely risk-sensitiveness. We present novel single and multi-objective criteria to optimize feature reduction, effectiveness, and risk-sensitiveness, all at the same time. We also introduce a new methodology to explore the search space, suggesting effective and efficient extensions of a well-known Evolutionary Algorithm (SPEA2) for FS applied to L2R. Our experiments show that explicitly including risk as an objective criterion is crucial to achieving a more effective and risk-sensitive performance. We also provide a thorough analysis of our methodology and experimental results.
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