Category-aware Location Embedding For Point-of-interest Recommendation
Hossein A. Rahmani, Mohammad Aliannejadi, Rasoul Mirzaei Zadeh, Mitra Baratchi, Mohsen Afsharchi, Fabio Crestani . Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval 2019 – 26 citations
[Paper]
Recently, Point of interest (POI) recommendation has gained ever-increasing importance in various Location-Based Social Networks (LBSNs). With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a better recommendation. However, previous studies fail to capture crucial information about POIs such as categorical information. In this paper, we propose a novel neural model that generates a POI embedding incorporating sequential and categorical information from POIs. Our model consists of a check-in module and a category module. The check-in module captures the geographical influence of POIs derived from the sequence of users’ check-ins, while the category module captures the characteristics of POIs derived from the category information. To validate the efficacy of the model, we experimented with two large-scale LBSN datasets. Our experimental results demonstrate that our approach significantly outperforms state-of-the-art POI recommendation methods.