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.

Personalized Neural Usefulness Network for Rating Prediction Reviews are crucial to rating prediction on ecommerce websites such as Amazon and Yelp. They reflect user preferences and item properties. Considering not all parts of the reviews are equally important for rating prediction, some works select the more informative words of reviews, others focus on finding the more useful reviews for each user and item. However, on the one hand, the meaning of words may be incomplete, further they may distort the original meaning of sentences, on the other hand, for the more useful reviews, they still contain more useful parts and less useful parts for rating prediction. To address these problems, we propose to model reviews in a view of sentence usefulness, since different sentences have varied importance for reviews modeling. In addition, the preferences of a user may vary with different item properties, it leads to the inconstant usefulness of a user sentence for different items, and vice verse. Hence, in this paper, we show a Personalized Neural Usefulness Network (PUN) to capture the varied sentence usefulness of reviews for rating prediction. Expensive experiments on benchmark datasets demonstrate that PUN significantly outperforms the stateoftheart baseline models. In the meantime, according to personalized sentence usefulness, PUN also attains highlyuseful sentences to better understand the varied user preferences and item properties.