Joint Deep Modeling of Users and Items Using Reviews for Recommendation
This work addresses the sparsity problem in recommender systems for users and platforms by incorporating review text, though it is incremental as it builds on existing neural and factorization methods.
The paper tackles the problem of improving recommendation quality by leveraging user reviews to address data sparsity, resulting in a model that significantly outperforms baseline recommender systems across multiple datasets.
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.