Exploiting deep transformer models in textual review based recommender systems

Shivangi Gheewala, Shuxiang Xu, Soonja Yeom, Sumbal Maqsood

Abstract: Textual reviews contain fine-grained information that can effectively infer user preferences over the items. Accordingly, the latest studies in the field of recommender systems exploit content-rich review texts to complement user and item representations and improve the ability to make personalized recommendations. Furthermore, the interactive deep learning mechanism can better model the user-item interaction from fine-grained textual reviews compared to traditional recommendation approaches improving the predictive performances. Therefore, it becomes important to investigate the design of existing deep learning methods for review-based recommender systems and innovate to make them capable of meeting desired recommendation schemes. The purpose of this research is to explore the performance of deep learning networks and deep transformer models in review-based recommender systems. In this paper, we conduct a compendious survey of the latest deep learning techniques in review-based recommender systems. Then investigation calls to employ and analyze deep transformer models for the review-based recommender systems. The wide range of experiments shows that deep transformer models can extract interpretable and relevant user/item representations than traditional deep learning networks. The findings indicate that the best deep transformer performance gains the maximum relative improvement (RMSE = 4.6%, MAE = 7.4%) with Amazon electronics, compared to the best outcome from traditional deep learning networks. In the end, this article highlights research gaps and outlines research opportunities for future research in this field.