A Deep Neural Network for Crossing-City POI Recommendations

With the popularity of location-aware devices e.g. smart phones), large amounts of location-based social media data such as check-ins are generated, which stimulates plenty of studies for POI recommendations by applying machine learning techniques. However, most of the existing studies focus on POI recommendations in the same city or region, and fail to recommend POIs for users when they travel to a new city. In this paper, we propose a novel deep neural network, named as ST-TransRec, for crossing-city POI recommendations which integrates the deep neural network, transfer learning technique, and density-based resampling method into a unified framework. In this model, the deep neural network is used to capture users' preferences for POIs and predict the textual context of POIs to enhance the embeddings of POIs. Besides, we employ the transfer learning technique to bridge the gap between cities which results from the city-dependent features. To address the imbalanced distribution over POIs, we designed a density-based spatial resampling model. In this way, POIs can be well matched across cities. We conduct extensive experiments on two real-world datasets. The experimental results show the advantages of ST-TransRec over the state-of-the-art methods for crossing-city POI recommendations.