Groupwise Learning to Rank Algorithm with Introduction of Activated Weighting

Abstract

Learning to rank (LtR) applies supervised machine learning (SML) technologies to the ranking problems, aiming at optimizing the relevance of input document list. As regard to previous studies on the deep ranking model, the calculation of the relevance of the documents in the list is independent of each other, which lacks consideration of document interactions. In recent years, some new methods are devoted to mining the interaction between documents, such as groupwise scoring function (GSF), which learns multivariate scoring function to jointly judge the correlation, but most of these methods ignore the differences of the interaction between documents, and bring high calculation cost at the same time. In order to solve this problem, this paper proposes a weighted groupwise deep ranking model (W-GSF). In view of the deep interest network in the field of recommendation, this paper intro-duces the idea of adjusting the weight of historical behavior sequence according to the candidate products. On the basis of multivariate scoring method in learning to rank field, this method uses muti-layer feed forword neural networks as main structure, and adds an activation unit into it before the input module, taking advantage of neural networks to adjust the weight of input multiple variables adaptively, so as to mine the differences of cross document relationship. Experiments on the public benchmark dataset MSLR verify the effectiveness of the method. Compared with baseline ranking models, the introduction of activation strategy brings a significant improvement of ranking metrics, and the computational complexity is greatly reduced compared with the same effect learning to rank methods.

Keywords

• |learning to rank (ltr)|groupwise scoring function (gsf)|deep neural network|deep interest network