Explainable Restaurant Closure Prediction through Co-Attentive Contrastive Learning

In this paper, we propose a novel approach to enhance user and restaurant representations in the context of predicting the closure of a restaurant and give an explanation based on data generated from user-restaurant interactions. In order to accurately predict the operating status of a restaurant and give a reasonable explanation, we need rich relevant information of user-restaurant interaction and reviews to model their representations. However, the interaction information between the user and the restaurant is usually sparse. To address this issue, we propose a new model, which is called the Co-Attentive Contrastive Learning (CACL) model. Our model employs a contrastive learning algorithm to deal with data sparsity and leverages co-attention mechanism to select the most relevant review information assisting the former to obtain a more accurate and granular representation. By fusing these module, we obtain rich information to perform two tasks with better quality. To demonstrate the effectiveness of our model, extensive experiments was conducted in six different cities, and the results showed that CACL was superior to the previous method in terms of prediction accuracy and explainable ability (average 4.8% improvement in prediction and 35.4% improvement in explanation).