图 书 馆
基于半监督二元多目标张量分解的审查滥用检测Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition |
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| 课程网址: | http://videolectures.net/kdd2019_yelundur_chaoji_mishra/ |
| 主讲教师: | Anil Yelundur |
| 开课单位: | 亚马逊 |
| 开课时间: | 2020-03-02 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Product reviews and ratings on e-commerce websites provide customers with detailed insights about various aspects of the product such as quality, usefulness, etc. Since they influence customers’ buying decisions, product reviews have become a fertile ground for abuse by sellers (colluding with reviewers) to promote their own products or to tarnish the reputation of competitor’s products. In this paper, our focus is on detecting such abusive entities (both sellers and reviewers) by applying tensor decomposition on the product reviews data. While tensor decomposition is mostly unsupervised, we formulate our problem as a semi-supervised binary multi-target tensor decomposition, to take advantage of currently known abusive entities. We empirically show that our multi-target semi-supervised model achieves higher precision and recall in detecting abusive entities as compared to unsupervised techniques. Finally, we show that our proposed stochastic partial natural gradient inference for our model empirically achieves faster convergence than stochastic gradient and Online-EM with sufficient statistics. |
| 关 键 词: | 基于半监督二元多目标; 多目标张量分解; 审查滥用检测 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-09-19：cyh |
| 最后编审: | 2022-09-19：cyh |
| 阅读次数: | 33 |