Towards real-time demand-aware sequential POI recommendation

Next point-of-interest (POI) recommendation has gained growing attention in recent years due to the emergence of location-based social networks (LBSN) services. Most existing approaches focus on learning user's preferences to POIs from check-in records and recommend a POI to visit next given his/her previously visited POIs. However, the user's visiting behavior is not only driven by user preferences in real-world scenarios. The real-time demand is another crucial factor to determine the user's visiting behaviors, which is usually neglected in established approaches. In this paper, we propose a new next point-of interest (POI) recommendation method, called DSPR, by exploring user's preferences and real-time demand simultaneously. To model the real-time demand, different kinds of contextual information are exploited, such as absolute time, POI-POI transition time/distance, and the types of POIs. By incorporating user's preferences, these contextual factors are further modeled and learned automatically with an attention-based recurrent neural network model to support the final next POI recommendation. Experiments on three real-world check-in datasets show that DSPR has better recommendation performance compared with many state-of-the-art methods. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a new method for next point-of-interest (POI) recommendation, called DSPR, by exploring user preferences and real-time demand simultaneously to support the final POI recommendation. Experimental results show that DSPR outperforms many state-of-the-art methods in recommendation performance.