Huang, J., & Zhang, T. (2021). Personalized POI recommendation using deep reinforcement learning. In A. Basiri, G. Gartner, & H. Huang (Eds.), LBS 2021: Proceedings of the 16th International Conference on Location Based Services (pp. 142–148). https://doi.org/10.34726/1761
POI recommendation; deep reinforcement learning; graph embedding
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Abstract:
As an important location based service, next Point-Of-Interest
(POI) recommendation has been widely utilized in helping people discover
attractive and interesting locations. However, the sparsity of check-in data,
the cold start issue and complicated contextual and semantic relationships
between users and POIs bring severe challenges. To cope with these
challenges, we develop a novel deep reinforcement learning based
personalized POI recommendation framework. Within the proposed
framework, a joint graph embedding model is proposed to compute users’
dynamic preferences, accounting for inter-user relationships, historical
check-in sequence, and category information of visited POIs. We are
working on the experiments of applying the proposed POI recommendation
framework on two typical real-world location-based social network datasets.
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Weitere Information:
Published in “Proceedings of the 16th International Conference on
Location Based Services (LBS 2021)”, edited by Anahid Basiri, Georg
Gartner and Haosheng Huang, LBS 2021, 24-25 November 2021,
Glasgow, UK/online
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