A Review Selection Approach for Accurate Feature Rating Estimation
In this paper, we propose a review se-lection approach towards accurate esti-mation of feature ratings for services on participatory websites where users write textual reviews for these services. Our approach selects reviews that compre-hensively talk about a feature of a service by using information distance of the re-views on the feature. The rating estima-tion of the feature for these selected re-views using machine learning techniques provides more accurate results than that for other reviews. The average of these estimated feature ratings also better rep-resents an accurate overall rating for the feature of the service, which provides useful feedback for other users to choose their satisfactory services.
Chong Long Jie Zhang Xiaoyan Zhu
State Key Laboratory on Intelligent Technology and Systems,Tsinghua National Laboratory for Informat School of Computer Engineering, Nanyang Technological University State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Informa
国际会议
The 23rd International Conference on Computational Linguistics(第23届国际计算语言学大会)
北京
英文
766-774
2010-08-01（万方平台首次上网日期，不代表论文的发表时间）