Cognitive process-driven model design: A deep learning recommendation model with textual review and context

Abstract

Online reviews play a crucial role in comprehending user rating behavior and improving personalized recommendations in e-commerce. However, existing review-based recommendation systems ignore the influence of theory-driven and context information. This paper proposes the Deep Learning Recommendation Model with Textual Review and Context (DeepRM-TC), which is built upon a cognitive process-driven approach to improve the quality and interpretability of recommendations. The DeepRM-TC framework mimics the human brain's cognitive process for predicting user rating behavior. Essentially, the simulation of human cognitive processing is manifested in several aspects of the designed neural network, including treating rating prediction to an attitude question, mapping raw data in latent space as the user's belief, injecting attention mechanisms for rendering judgment and predicting rating as an answer. Furthermore, we design a three-layer coattention mechanism to adaptively match product information based on users' preferences in various contexts. This mechanism extracts finer-grained interaction information from user–product–context pairs. Extensive experiments on real datasets demonstrate that our proposed model outperforms existing state-of-the-art models. We demonstrate the importance of context information and the three-layer coattention mechanism in enhancing recommendation accuracy through ablation and hierarchic analysis, respectively. Additionally, we further validate the performance of our model through data sparseness analysis, scalability analysis, other datasets, classification analysis, and user study.