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.