An information gain-based approach for recommending useful product reviews
An information gain-based approach for recommending useful product reviews
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DOI：
10.1007/s10115-010-0287-y
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发表时间：
2011-03
影响因子：
2.7
通讯作者：
Richong Zhang;T. Tran
中科院分区：
文献类型：
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作者：
Richong Zhang;T. Tran
最近，许多电子商务网站，如Amazon.com，为用户提供了审查产品和分享他们的意见的平台，以帮助消费者做出最佳购买决定。然而，除非消费者仔细分析大量冗长的评论，否则不同产品评论的质量和有用程度不会透露给消费者。考虑到大量可用的在线产品评论，这对任何消费者来说都是一项不可能完成的任务。因此，开发能够有效评估在线产品评论并向消费者推荐最有用的产品评论的推荐系统是至关重要的。本文提出了一个基于信息增益的模型来预测在线产品评论的有用性，目的是向消费者推荐最合适的产品和供应商。通过我们的评分模型对评论进行分析和排名，发现对消费者有更好帮助的评论。此外，我们还将我们的模型与几种机器学习算法进行了比较。我们的实验结果表明，我们的方法是有效的在线产品评论的排名和分类。
Recently, many e-commerce Web sites, such as Amazon.com, provide platforms for users to review products and share their opinions, in order to help consumers make their best purchase decisions. However, the quality and the level of helpfulness of different product reviews are not disclosed to consumers unless they carefully analyze an immense number of lengthy reviews. Considering the large amount of available online product reviews, this is an impossible task for any consumer. Therefore, it is of vital importance to develop recommender systems that can evaluate online product reviews effectively to recommend the most useful ones to consumers. This paper proposes an information gain-based model to predict the helpfulness of online product reviews, with the aim of suggesting the most suitable products and vendors to consumers. Reviews are analyzed and ranked by our scoring model and reviews that help consumers better than others will be found. In addition, we also compare our model with several machine learning algorithms. Our experimental results show that our approach is effective in ranking and classifying online product reviews.