Multimodal review helpfulness prediction with a multi-level cognitive reasoning mechanism: A theory-driven graph learning model

Customers' perception of review helpfulness entails a cognitive reasoning process influenced by the contextual information of reviews including product descriptions and review neighbors. Current studies on helpfulness prediction primarily focus on static features of individual reviews, neglecting the dynamic interaction among products, reviews and their contextual neighbors. To address this gap, we propose a theory-driven deep learning model for multimodal review helpfulness prediction (DeepMRHP-MCR). The model can collectively simulate human cognitive processes when voting on whether a review is helpful. Specifically, this study presents a multi-level cognitive reasoning mechanism that reconciles the inconsistencies among product descriptions, reviews and their neighbors from the modality, individual and contextual level, respectively. A case study is conducted on the real-world datasets collected from Amazon.com. Empirical results show that the proposed model can improve the quality and interpretability of review prediction process, and present a deep comprehension of consumer's cognitive decision-making process when evaluating reviews.