Personalized POI Recommendation Based on Subway Network Features and Users’ Historical Behaviors

Abstract

Current recommender systems often take fusion factors into consideration to realize personalize point-of-interest (POI) recommendation. Historical behavior records and location factors are two kinds of significant features in most of recommendation scenarios. However, existing approaches usually use the Euclidean distance directly without considering the traffic factors. Moreover, the timing characteristics of users’ historical behaviors are not fully utilized. In this paper, we took the restaurant recommendation as an example and proposed a personalized POI recommender system integrating the user profile, restaurant characteristics, users’ historical behavior features, and subway network features. Specifically, the subway network features such as the number of passing stations, waiting time, and transfer times are extracted and a recurrent neural network model is employed to model user behaviors. Experiments were conducted on a real-world dataset and results show that the proposed method significantly outperforms the baselines on two metrics.