Opinion Recommendation Using A Neural Model

Zhongqing Wang, Yue Zhang


Abstract
We present opinion recommendation, a novel task of jointly generating a review with a rating score that a certain user would give to a certain product which is unreviewed by the user, given existing reviews to the product by other users, and the reviews that the user has given to other products. A characteristic of opinion recommendation is the reliance of multiple data sources for multi-task joint learning. We use a single neural network to model users and products, 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. our methods give better results compared to several pipelines baselines.
Anthology ID:
D17-1170
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1626–1637
Language:
URL:
https://www.aclweb.org/anthology/D17-1170
DOI:
10.18653/v1/D17-1170
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1170.pdf
Video:
 https://vimeo.com/238235088