Joint Deep Modeling of Users and Items Using Reviews for Recommendation

2017/01/17 by Lei Zheng, Zheng, Lei, Vahid Noroozi +3 · 27 citations
Computer Science · #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Recommender Systems and Techniques #Sentiment Analysis and Opinion Mining #Topic Modeling #cs.IR #cs.LG

paper · pdf · doi:10.48550/arxiv.1701.04783

WSDM 2017

arxiv created 2017/01/17 · openalex publication_date 2017/01/17 · arxiv updated 2017/01/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Matrix Factorization Techniques for Recommender Systems
• Convolutional Neural Networks for Sentence Classification
• Learning Attitudes and Attributes from Multi-Aspect Reviews

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• Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works
• LLM-Derived Priors for Thompson Sampling in Cold-Start Comment Recommendation
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Related

• Integrating User and Item Reviews in Deep Cooperative Neural Networks for Movie Recommendation
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