Joint Deep Modeling of Users and Items Using Reviews for Recommendation

TL;DR

This paper introduces DeepCoNN, a deep learning model that jointly learns user behaviors and item properties from reviews to enhance recommendation accuracy, effectively addressing data sparsity issues.

Contribution

The paper proposes a novel deep neural network architecture that integrates review data for improved user-item modeling in recommender systems.

Findings

DeepCoNN outperforms baseline recommenders across multiple datasets.

Joint modeling of users and items from reviews improves recommendation quality.

Shared layers enable effective interaction between user and item latent factors.

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.