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总览 评价 郑海涛 ， 李琪 ， 江勇 ， 夏树涛 （ 清华大学深圳研究生院信息学部,深圳 518055； ） 摘要： 专家查找是标识关于某一主题专家的过程。在这篇论文中,我们提出了一种基于排序学习的专家查找方法 (LREF, Learning to Rank for Expert Finding),尝试
郑海涛， 李琪， 江勇， 夏树涛
（
清华大学深圳研究生院信息学部,深圳 518055； ）
摘要：
专家查找是标识关于某一主题专家的过程。在这篇论文中,我们提出了一种基于排序学习的专家查找方法 (LREF, Learning to Rank for Expert Finding),尝试使用排序学习来提高专家查找的精度。排序学习是一种公认的预测排序的方法并且最近在信息检索领域表现不俗。LREF首先定义了主题和专家的表示方法,然后从这些表示中抽取语言模型和基本的文档特征形成特征向量用于学习。最后,LREF采用RankSVM,一种两两成对的(pairwise)排序学习算法,来生产主题相应专家的列表。通过实验与现在常用的语言模型（基于轮廓的模型和基于文档的模型）相比,LREF提高了专家查找的精度。
关键词：
专家查找；语言模型；排序学习；特征
ZHENG Hai-Tao， Li Qi， JIANG Yong， XIA Shu-Tao
（
Department of Computer Science, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055 ； ）
Abstract：
Expert finding is the process of identifying experts given a particular topic. In this paper, we propose a method called Learning to Rank for Expert Finding (LREF) attempting to leverage learning to rank to improve the estimation for expert finding. Learning to rank is an established means of predicting ranking and has recently demonstrated high promise in information retrieval. LREF first defines representations for both topics and experts, and then collects the existing popular language models and basic document features to form feature vectors for learning purpose from the representations. Finally, LRER adopts RankSVM, a pair wise learning to rank algorithm, to generate the lists of experts for topics. Extensive experiments in comparison with the language models (profile based model and document based model), which are state-of-the-art expert finding methods, show that LREF enhances expert finding accuracy.
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