Customer Reviews Analysis With Deep Neural Networks for E-Commerce Recommender Systems

An essential prerequisite of an effective recommender system is providing helpful information regarding users and items to generate high-quality recommendations. Written customer review is a rich source of information that can offer insights into the recommender system. However, dealing with the customer feedback in text format, as unstructured data, is challenging. In this research, we extract those features from customer reviews and use them for similarity evaluation of the users and ultimately in recommendation generation. To do so, we developed a glossary of features for each product category and evaluated them for removing irrelevant terms using Latent Dirichlet Allocation. Then, we employed a deep neural network to extract deep features from the reviews-characteristics matrix to deal with sparsity, ambiguity, and redundancy. We applied matrix factorization as the collaborative filtering method to provide recommendations. As the experimental results on the Amazon.com dataset demonstrate, our methodology improves the performance of the recommender system by incorporating information from reviews and produces recommendations with higher quality in terms of rating prediction accuracy compared to the baseline methods.