期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Semantic-Oriented Knowledge Transfer for Review Rating 被引量:1
1
作者 王波 张宁 +2 位作者 林泉 陈松灿 李玉华 《Tsinghua Science and Technology》 SCIE EI CAS 2010年第6期633-641,共9页
With the rapid development of Web 2.0, more and more people are sharing their opinions about online products, so there is much product review data. However, it is difficult to compare products directly using ratings b... With the rapid development of Web 2.0, more and more people are sharing their opinions about online products, so there is much product review data. However, it is difficult to compare products directly using ratings because many ratings are based on different scales or ratings are even missing. This paper addresses the following question: given textual reviews, how can we automatically determine the semantic orientations of reviewers and then rank different items? Due to the absence of ratings in many reviews, it is difficult to collect sufficient rating data for certain specific categories of products (e.g., movies), but it is easier to find rating data in another different but related category (e.g., books). We refer to this problem as transfer rating, and try to train a better ranking model for items in the interested category with the help of rating data from another related category. Specifically, we developed a ranking-oriented method called TRate for determining the semantic orientations and for ranking different items and formulated it in a regularized algorithm for rating knowledge transfer by bridging the two related categories via a shared latent semantic space. Tests on the Epinion dataset verified its effectiveness. 展开更多
关键词 review rating latent semantic space transfer rating
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部