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香港城市大学 王军辉副教授:Query-dependent learning to rank

([西财新闻] 发布于 :2017-04-14 )

光华讲坛——社会名流与企业家论坛第4448期

 

  题:Query-dependent learning to rank

主讲人:香港城市大学  王军辉 副教授

主持人:吕绍高 副教授

  间:20170417日(星期一)上午10:00-11:00

  点:腾骧楼101会议室

主办单位:统计研究中心  统计学院   科研处

 

主讲人简介

王军辉教授现为香港城市大学数学系副教授兼副系主任。王教授毕业于美国明尼苏达大学获统计学博士学位,并曾在美国哥伦比亚大学以及伊利诺伊大学芝加哥分校担任教职。王教授的研究方向包括统计机器学习,大规模文本数据挖掘,模型选择以及变量选择,并曾发表学术论文40余篇,包括数篇JASABiometrikaJMLR等顶尖的统计及机器学习杂志。

 

内容提要:

Learning to rank is central to many information retrieval applications, ranging from document retrieval, expert search, computational advertising, to sentiment analysis. Taking document retrieval as an illustrating example, the primary goal is to rank a large collection of text documents given a text-based query, and retrieve the top-ranked documents. In this talk, I will present a query-specific learning to rank model that admits different ranking functions for different queries and also incorporates neighborhood structure among queries to improve the ranking performance. As opposed to most existing ranking models assuming a common ranking function for all queries, one key advantage of the proposed query-specific ranking model is that it can vary from query to query and thus accommodates the heterogeneity among different queries. The advantage is confirmed in a variety of simulated experiments as well as one large-scale real example on Yahoo! ranking challenge. If time permits, the asymptotic properties will also be discussed.

 

 

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