Reason Generation for Point of Interest Recommendation Via a Hierarchical Attention-Based Transformer Model

Existing point-of-interest (POI) recommendation methods only show the direct recommendation results and lack the proper reasons for recommendation. In recent years, explainable recommendation has become an increasingly important subfield in recommendation systems. The aim of explainable recommendation is to provide a reason why an item is recommended to a user. In this way, it helps to improve the transparency, persuasiveness and user satisfaction of recommendation systems. The explainable recommendation should indicate users’ preferences for POIs, such as the category and the price. In addition, to increase the diversity of the results, we take emotional intensity into account in our model to generate more vivid reasons. To this end, we propose a hierarchical attention-based transformer model to generate reasons with specific topics and different emotions. With a hierarchical attention mechanism, we can capture the word-level and attribute-level preferences of users. In addition, we also learn the latent representation of the emotion score to generate diverse recommendation reasons. We evaluate the proposed model on a new real-world dataset collected from three travel service websites. The experimental results demonstrate that our method outperforms the related approaches for reason generation.