Learning to rank: from pairwise approach to listwise approach

The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects. Learning to rank is useful for document retrieval, collaborative filtering, and many other applications. Several methods for learning to rank have been proposed, which take object pairs as `instances' in learning. We refer to them as the pairwise approach in this paper. Although the pairwise approach offers advantages, it ignores the fact that ranking is a prediction task on list of objects. The paper postulates that learning to rank should adopt the listwise approach in which lists of objects are used as `instances' in learning. The paper proposes a new probabilistic method for the approach. Specifically it introduces two probability models, respectively referred to as permutation probability and top k probability, to define a listwise loss function for learning. Neural Network and Gradient Descent are then employed as model and algorithm in the learning method. Ex...

• Type: Conference
• Year: 2007
• Where: ICML
• Authors: Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Hang Li
• Keywords: Approach Offers Advantages, ICML 2007, Learning Method, Machine Learning, Pairwise Approach