6 research outputs found
Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition
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.Comment: Accepted to the 25th ACM SIGKDD Conference on Knowledge Discovery and
Data Mining, 2019. Contains supplementary material. arXiv admin note: text
overlap with arXiv:1804.0383
Directional Multivariate Ranking
User-provided multi-aspect evaluations manifest users' detailed feedback on
the recommended items and enable fine-grained understanding of their
preferences. Extensive studies have shown that modeling such data greatly
improves the effectiveness and explainability of the recommendations. However,
as ranking is essential in recommendation, there is no principled solution yet
for collectively generating multiple item rankings over different aspects. In
this work, we propose a directional multi-aspect ranking criterion to enable a
holistic ranking of items with respect to multiple aspects. Specifically, we
view multi-aspect evaluation as an integral effort from a user that forms a
vector of his/her preferences over aspects. Our key insight is that the
direction of the difference vector between two multi-aspect preference vectors
reveals the pairwise order of comparison. Hence, it is necessary for a
multi-aspect ranking criterion to preserve the observed directions from such
pairwise comparisons. We further derive a complete solution for the
multi-aspect ranking problem based on a probabilistic multivariate tensor
factorization model. Comprehensive experimental analysis on a large TripAdvisor
multi-aspect rating dataset and a Yelp review text dataset confirms the
effectiveness of our solution.Comment: Accepted as a full research paper in KDD'2