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

arXiv: https://arxiv.org/abs/1907.13376 · HTML: https://mlsanity.com/paper/1907.13376/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2019.

## Built on by (most cited first, 8 of 8)

- [Automated Spatio-Temporal Graph Contrastive Learning](https://mlsanity.com/paper/2305.03920/) (2023)
- [Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation](https://mlsanity.com/paper/2306.10683/) (2023)
- [Self Contrastive Learning for Session-based Recommendation](https://mlsanity.com/paper/2306.01266/) (2023)
- [HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning](https://mlsanity.com/paper/2410.10915/) (2024)
- [Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation](https://mlsanity.com/paper/2201.03450/) (2022)
- [A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation](https://mlsanity.com/paper/2201.08150/) (2022)
- [Understanding mobility in networks: A node embedding approach](https://mlsanity.com/paper/2111.06161/) (2021)
- [Can We Predict Your Next Move Without Breaking Your Privacy?](https://mlsanity.com/paper/2507.08843/) (2025)

## Abstract (arXiv)

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.