Title: Opinion Recommendation using Neural Memory Model,
Abstract: We present opinion recommendation, a novel task of jointly predicting a
custom review with a rating score that a certain user would give to a certain
product or service, given existing reviews and rating scores to the product or
service by other users, and the reviews that the user has given to other
products and services. A characteristic of opinion recommendation is the
reliance of multiple data sources for multi-task joint learning, which is the
strength of neural models. We use a single neural network to model users and
products, capturing their correlation and generating customised product
representations using a deep memory network, from which customised ratings and
reviews are constructed jointly. Results show that our opinion recommendation
system gives ratings that are closer to real user ratings on Yelp.com data
compared with Yelp's own ratings, and our methods give better results compared
to several pipelines baselines using state-of-the-art sentiment rating and
summarization systems.