From Interaction to Prediction: A Multi-Interactive Attention-Based Approach to Product Rating Prediction

Despite increasing research on product rating prediction, very few studies have considered user-item interaction relationships at multiple levels. To address this critical limitation, we propose a novel rating prediction method based on multi-interaction attention (RPMIA) by learning user-item interaction relationships at three levels simultaneously from online consumer reviews for predicting product ratings with reasonable interpretability. Specifically, RPMIA first deploys a multihead cross-attention mechanism to capture the interaction between contexts of items and users. Then, it uses a bilayer gate-based mechanism to extract the aspects of items and users and a self-attention mechanism to learn their interaction at the aspect level. Finally, the aspects of users and items are coupled together to form meaningful user-item aspect pairs via a joint attention. A multitask predictor that integrates a factorization machine and a feedforward neural network is designed to generate a rating prediction. We empirically evaluated RPMIA with seven real-world data sets. The results demonstrate that RPMIA outperforms the state-of-the-art methods consistently and significantly. We also conduct a user study to assess the interpretability of the RPMIA method. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: The research is supported by Beijing Social Science Foundation [24XCB012], Suzhou Key Laboratory of Artificial Intelligence and Social Governance Technologies [SZS2023007], and Smart Social Governance Technology and Innovative Application Platform [YZCXPT2023101]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0131 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0131 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .