UNCERTAINTY-BASED ACTIVE RANKING FOR DOCUMENT RETRIEVAL

摘要：

One of the main problems in information retrieval is ranking documents according to their relevance to users queries. Learning to rank is considered as a promising approach for addressing the issue. However, like many other supervised approaches, one of the main problems with learning to rank is the lack of labeled data, as well as labeling instances to create a rank model is time-consuming and costly. Thus, it is beneficial to minimize the number of labeled instances. In this paper, we bring the idea of active learning into ranking problem, and propose a new active ranking approach for document retrieval, referred to as Active RSVM. Specifically, we present an uncertainty- based query function to estimate the uncertainty of each instance, decide which instances can provide more information for the ranker and reduce the labeling cost. Experimental results on two real-world datasets show that our proposed active ranking algorithm can reduce the labeling cost greatly without decreasing the ranking accuracy.

关键词： Information Retrieval Learning to Rank Active Learning Ranking SVM Query Function

作者: YANG WANG YU-HAO KUAI YA-LOU HUANG DONG LI WEI-JIAN NI

作者单位: College of Software, Nankai University, Tianjin 300071, China College of Information Technology Science, Nankai University, Tianjin 300071, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 2629-2634

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）