2017 JointDeepModelingofUsersandItem

• (Zheng et al., 2017) ⇒ Lei Zheng, Vahid Noroozi, and Philip S. Yu. (2017). “Joint Deep Modeling of Users and Items Using Reviews for Recommendation.” In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ISBN:978-1-4503-4675-7 doi:10.1145/3018661.3018665

Subject Headings: Deep Learning-based Recommendation.

Cited By

• http://scholar.google.com/scholar?q=%222017%22+Joint+Deep+Modeling+of+Users+and+Items+Using+Reviews+for+Recommendation
• http://dl.acm.org/citation.cfm?id=3018661.3018665&preflayout=flat#citedby

Quotes

Abstract

A large amount of information exists in reviews written by users. This source of information has been ignored by most of the current recommender systems while it can potentially alleviate the sparsity problem and improve the quality of recommendations. In this paper, we present a deep model to learn item properties and user behaviors jointly from review text. The proposed model, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers. One of the networks focuses on learning user behaviors exploiting reviews written by the user, and the other one learns item properties from the reviews written for the item. A shared layer is introduced on the top to couple these two networks together. The shared layer enables latent factors learned for users and items to interact with each other in a manner similar to factorization machine techniques. Experimental results demonstrate that DeepCoNN significantly outperforms all baseline recommender systems on a variety of datasets.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2017 JointDeepModelingofUsersandItem	Philip S. Yu Lei Zheng Vahid Noroozi			Joint Deep Modeling of Users and Items Using Reviews for Recommendation				10.1145/3018661.3018665		2017