Name
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 Yelpu0027s own ratings, and our methods give better results compared to several pipelines baselines using state-of-the-art sentiment rating and summarization systems. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computation and Language | Recommender system,Automatic summarization,Multiple data,Computer science,Baseline (configuration management),Memory model,Artificial intelligence,Artificial neural network,Machine learning |
DocType | Volume | Citations |
Journal | abs/1702.01517 | 2 |
PageRank | References | Authors |
0.40 | 24 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhong-qing Wang | 1 | 140 | 20.28 |
| Yue Zhang | 2 | 1364 | 114.17 |