A Query Dependent Approach to Learning to Rank for Information Retrieval
This paper proposes a new ranking approach for information retrieval, where the diversity among queries are taken into consideration. In information retrieval, the usersqueries often vary a lot from one to another, so that the documents retrieved from different queries are also distributed differently. Due to this diversity, it is not appropriate to assume all the documents to be ranked are generated i.i.d. (independently and identically distributed) according to a.xed but unknown probability distribution. However, most of the existing learning to rank approaches are proposed on the basis of the conventional i.i.d. assumption. In this paper, the conventional i.i.d. assumption is relaxed to fit the real situations of information retrieval better, and then a new ranking approach, referred to as Query Dependent Ranking is proposed. In our approach, the ranking models for different queries have generality while each of them has its own speciality. The experimental results on both synthetic and real-world datasets show the advantage of our approach to conventional ranking approaches.
Weijian Ni Yalou Huang Maoqiang Xie
College of Information Technical Science Nankai University No.94 Weijin Road,Tianjin,China College of Software Nankai University No.94 Weijian Road,Tianjin,China
国际会议
The Ninth International Conference on Web-Age Information Management(第九届web时代信息管理国际会议)(WAIM 2008)
张家界
英文
2008-07-20（万方平台首次上网日期，不代表论文的发表时间）