Learning Review Representations from user and Product Level Information for Spam Detection Opinion spam has become a widespread problem in social media, where hired spammers write deceptive reviews to promote or demote products to mislead the consumers for profit or fame. Existing works mainly focus on manually designing discrete textual or behavior features, which cannot capture complex global semantics of reviews. Although recent works apply deep learning methods to learn reviewlevel semantic features, their models ignore the impact of the userlevel and productlevel information on learning review semantics and the inherent userreviewproduct relationship information. In this paper, we propose a Hierarchical Fusion Attention Network (HFAN) to automatically learn the semantics of reviews from user and product level. Specifically, we design a multiattention unit to extract user(product)related review information. Then, we use orthogonal decomposition and fusion attention to learn a user, review, and product representation from the review information. Finally, we take the review as a relation between user and product entity and apply TransH to jointly encode this relationship into review representation. Experimental results obtained more than 10% absolute precision improvement over the stateoftheart performances on four realworld datasets, which show the effectiveness and versatility of the model.

Discovering Relevant Reviews for Answering ProductRelated Queries With the increasing popularity of ecommerce, the number of productrelated queries generated by customers is growing. Answering these queries manually in real time is infeasible, and so automatic questionanswering systems can be immensely helpful. Product queries are, however, very different from opendomain questions: they tend to be productspecific and the answers they demand can be very subjective. Previous research suggests that reviews are a valuable resource for answering product queries, but a key challenge is the language mismatch between user queries and reviews. To address this, we propose two neural models that discover relevant reviews for answering product queries. We demonstrate that our best model produces strong performance, outperforming stateoftheart systems by consistently finding the most relevant reviews for product queries.