Category-Aware Location Embedding for Point-of-Interest Recommendation

Recently, Point of interest (POI) recommendation has gained everincreasing 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. ACM Reference Format: Hossein A. Rahmani, Mohammad Aliannejadi, Rasoul Mirzaei Zadeh, Mitra Baratchi, Mohsen Afsharchi, and Fabio Crestani. 2019. Category-Aware Location Embedding for Point-of-Interest Recommendation. In The 2019 ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR ’19), October 2–5, 2019, Santa Clara, CA, USA. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3341981.3344240