Detecting Product Review Spammers Using Principles of Big Data
研究了如何利用大数据技术（Hadoop和Spark）从海量在线评论中识别垃圾信息发送者，提出了基于评分的模型和指数平滑方法，适用于大规模评论系统。
The growing consumerism has led to the importance of online reviews on the Internet. Opinions voiced by these reviews are taken into consideration by many consumers for making financial decisions online. This has led to the development of opinion spamming for profitable motives or otherwise. This work has been done to tackle the challenge of identifying such spammers, but the scale of the real-world review systems demands this problem to be tackled as a big data challenge. So, an effort has been made to detect online review spammers using the principle of big data. In this article, a rating-based model has been studied under the light of large-scale datasets (more than 80 million reviews by 20 million reviewers) using the Hadoop and Spark frameworks. Scale effects have been identified and mitigated to provide better context to large review systems. An improved computational framework has been presented to compute the overall spamcity of reviewers using exponential smoothing. The value of the smoothing factor was set empirically. Finally, future directions have been discussed.