PDPNN: Modeling User Personal Dynamic Preference for Next Point-of-Interest Recommendation

Abstract

Next Point of Interest (POI) recommendation is an important aspect of information feeds for Location Based Social Networks (LSBNs). The boom in LSBN platforms such as Foursquare, Twitter, and Yelp has motivated a considerable amount of research focused on POI recommendations within the last decade. Inspired by the success of deep neural networks in many fields, researchers are increasingly interested in using neural networks such as Recurrent Neural Network (RNN) to make POI recommendation. Compared to traditional methods like Factorizing Personalized Markov Chain (FPMC) and Tensor Factorization (TF), neural network methods show great improvement in general sequences prediction. However, the user’s personal preference, which is crucial for personalized POI recommendation, is not addressed well in existing works. Moreover, the user’s personal preference is dynamic rather than static, which can guide predictions in different temporal and spatial contexts. To this end, we propose a new deep neural network model called Personal Dynamic Preference Neural Network(PDPNN). The core of the PDPNN model includes two parts: one part learns the user’s personal long-term preferences from the historical trajectories, and the other part learns the user’s short-term preferences from the current trajectory. By introducing a similarity function that evaluates the similarity between spatiotemporal contexts of user’s current trajectory and historical trajectories, PDPNN learns the user’s personal dynamic preference from user’s long-term and short-term preferences. We conducted experiments on three real-world datasets, and the results show that our model outperforms current well-known methods.