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明星品质:汇总评论,对产品和商家进行排名

Star Quality: Aggregating Reviews to Rank Products and Merchants
课程网址: http://videolectures.net/icwsm2010_mcglohon_arr/  
主讲教师: Mary McGlohon
开课单位: 卡内基梅隆大学
开课时间: 2010-06-29
课程语种: 英语
中文简介:
鉴于对来自各种作者和多个评论网站的产品或商家的一系列评论,我们如何衡量产品或商家的真实质量?我们如何消除个别作者或来源的偏见?我们如何比较从不同网站获得的评论,评分可能在不同的等级(1 5星,A / B / C等)?我们如何筛选出不可靠的评论,仅使用具有“明星品质”的评论?考虑到这些因素,我们分析来自各种不同评论网站的数据集(第一篇论文,据我们所知,这样做)。这些数据集包括800万次产品评论和150万次商家评论。我们探索基于统计和启发式的模型来估计产品或商家的真实质量,并比较这些估算器在对对象排序任务上的表现。我们还将相同的模型应用于使用Netflix评级数据对电影对进行排名的任务,并发现不同模型的性能在此数据集上惊人地相似。
课程简介: Given a set of reviews of products or merchants from a wide range of authors and several reviews websites, how can we measure the true quality of the product or merchant? How do we remove the bias of individual authors or sources? How do we compare reviews obtained from different websites, where ratings may be on different scales (1-5 stars, A/B/C, etc.)? How do we filter out unreliable reviews to use only the ones with "star quality"? Taking into account these considerations, we analyze data sets from a variety of different reviews sites (the first paper, to our knowledge, to do this). These data sets include 8 million product reviews and 1.5 million merchant reviews. We explore statistic - and heuristic - based models for estimating the true quality of a product or merchant, and compare the performance of these estimators on the task of ranking pairs of objects. We also apply the same models to the task of using Netflix ratings data to rank pairs of movies, and discover that the performance of the different models is surprisingly similar on this data set.
关 键 词: 评论网站; 估算器; 数据集
课程来源: 视频讲座网
最后编审: 2019-04-26:lxf
阅读次数: 34