Estimating Reactions and Recommending Products with Generative Models of Reviews

Jianmo Ni, Zachary C. Lipton, Sharad Vikram, Julian McAuley


Abstract
Traditional approaches to recommendation focus on learning from large volumes of historical feedback to estimate simple numerical quantities (Will a user click on a product? Make a purchase? etc.). Natural language approaches that model information like product reviews have proved to be incredibly useful in improving the performance of such methods, as reviews provide valuable auxiliary information that can be used to better estimate latent user preferences and item properties. In this paper, rather than using reviews as an inputs to a recommender system, we focus on generating reviews as the model’s output. This requires us to efficiently model text (at the character level) to capture the preferences of the user, the properties of the item being consumed, and the interaction between them (i.e., the user’s preference). We show that this can model can be used to (a) generate plausible reviews and estimate nuanced reactions; (b) provide personalized rankings of existing reviews; and (c) recommend existing products more effectively.
Anthology ID:
I17-1079
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
783–791
Language:
URL:
https://aclanthology.org/I17-1079
DOI:
Bibkey:
Cite (ACL):
Jianmo Ni, Zachary C. Lipton, Sharad Vikram, and Julian McAuley. 2017. Estimating Reactions and Recommending Products with Generative Models of Reviews. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 783–791, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
Estimating Reactions and Recommending Products with Generative Models of Reviews (Ni et al., IJCNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/I17-1079.pdf