Opinion Recommendation using Neural Memory Model
This addresses the problem of generating customized recommendations for users in e-commerce or review platforms, though it is incremental as it builds on existing neural and recommendation techniques.
The paper tackles the task of opinion recommendation, which jointly predicts personalized reviews and ratings for a user-product pair by leveraging multiple data sources, and shows that their neural memory model outperforms Yelp's own ratings and several baseline systems on Yelp.com data.
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.