A sample partition method for learning to rank based on query-level vector extraction
Jungang Xu, Shilong Zhou, Hong Chen, Pengfei Li · 2015
Learning to rank plays a very important role in information retrieval. Existing works mainly focus on applying one ranking model to all samples, which may not be suitable for the reality. In this paper, a new method for learning to rank based on query-level vector extraction is proposed, in which we assume that all samples can be divided into multiple parts, and each part is used to train one set of parameters for the model. Based on this assumption, we extracted query-level vector and proposed a dataset partition method based on k-means++, which is used to optimize the ListNet method and RankNet method. Experimental results show that our assumption is right and our method plays a very important role in improving the performance of ListNet and RankNet, and which is also easy to be extended to other learning to rank methods.