Learning to rank using query-level regression

Authors:
Jiajin Wu;Zhihao Yang;Yuan Lin;Hongfei Lin;Zheng Ye;Kan Xu
Affiliations:
Dalian University of Technology, Dalian, China;Dalian University of Technology, Dalian, China;Dalian University of Technology, Dalian, China;Dalian University of Technology, Dalian, China;Dalian University of Technology, Dalian, China;Dalian University of Technology, Dalian, China
Venue:
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Year:
2011

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Abstract

In this paper, we use query-level regression as the loss function. The regression loss function has been used in pointwise methods, however pointwise methods ignore the query boundaries and treat the data equally across queries, and thus the effectiveness is limited. We show that regression is an effective loss function for learning to rank when used in query-level. We use neural network to model the ranking function and gradient descent for optimization and refer our method as ListReg. Experimental results show that ListReg significantly outperforms pointwise Regression and the state-of-the-art listwise method in most cases.