An integrated model based on deep multimodal and rank learning for point-of-interest recommendation

Modeling point-of-Interest (POI) for recommendations is vital in location-based social networks, yet it is a challenging task due to data sparsity and cold-start problems. Most existing approaches incorporate content features into a probabilistic matrix factorization model using unsupervised learning, which results in inaccuracy and weak robustness of recommendations when data is sparse, and the cold-start problems remain unsolved. In this paper, we propose a deep multimodal rank learning (DMRL) model that improves both the accuracy and robustness of POI recommendations. DMRL exploits temporal dynamics by allowing each user to have time-dependent preferences and captures geographical influences by introducing spatial regularization to the model. DMRL jointly learns ranking for personal preferences and supervised deep learning models to create a semantic representation of POIs from multimodal content. To make model optimization converge more rapidly while preserving high effectiveness, we develop a ranking-based dynamic sampling strategy to sample adverse or negative POIs for model training. We conduct experiments to compare our DMRL model with existing models that use different approaches using two large-scale datasets obtained from Foursquare and Yelp. The experimental results demonstrate the superiority of DMRL over the other models in creating cold-start POI recommendations and achieving excellent and highly robust results for different degrees of data sparsity.