A novel tourist attraction recommendation system based on improved visual Bayesian personalized ranking

Abstract

Statistics show that most tourists log into the main tourism websites to view user reviews or scores before selecting their destinations. However, the existing tourist destination recommendation models neither consider the implicit user preferences nor mine the potential semantics of tourist attractions. To solve the problems, this paper predicts user scores of tourist attractions through stratified sampling, and optimizes the predicted scores with Bayesian personalized ranking (BPR) and improved visual BPR (VBPR). Then, the recommendation system was optimized by the improved VBPR, which decomposes the prediction score matrix and considers visual features. Experimental results fully demonstrate the excellence of the proposed tourist attraction recommendation system. The research findings provide a good reference for online travel agencies to recommend tourist attractions.