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 tarnish the reputation of competitor's products. In this paper, our focus is on detecting such abusive entities (both sellers and reviewers) via a 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 with 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.

本文通过张量分解算法，提出了一种半监督二元多目标张量分解模型，用于检测电子商务网站上的商品评价领域中的虚假评论，并通过实验证明，相比于传统非监督技术，该模型在准确率和召回率上有更好的表现。此外，本文还提出了一种基于随机偏导自然梯度推断的算法，可以更快地收敛。

使用半监督二元多目标张量分解检测评论滥用