# Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation

arXiv: https://arxiv.org/abs/2001.08961 · HTML: https://mlsanity.com/paper/2001.08961/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2020.

## Built on by (most cited first, 11 of 11)

- [Cross-Market Product Recommendation](https://mlsanity.com/paper/2109.05929/) (2021)
- [The Unfairness of Popularity Bias in Book Recommendation](https://mlsanity.com/paper/2202.13446/) (2022)
- [The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation](https://mlsanity.com/paper/2202.13307/) (2022)
- [Self Contrastive Learning for Session-based Recommendation](https://mlsanity.com/paper/2306.01266/) (2023)
- [Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis](https://mlsanity.com/paper/2306.07664/) (2023)
- [Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation](https://mlsanity.com/paper/2311.00491/) (2023)
- [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)
- [Lexical Entrainment for Conversational Systems](https://mlsanity.com/paper/2310.09651/) (2023)
- [Point of Interest Recommendation: Pitfalls and Viable Solutions](https://mlsanity.com/paper/2507.13725/) (2025)
- [MoE-TransMov: A Transformer-based Model for Next POI Prediction in Familiar & Unfamiliar Movements](https://mlsanity.com/paper/2512.17985/) (2025)

## Abstract (arXiv)

With the popularity of Location-based Social Networks, Point-of-Interest (POI) recommendation has become an important task, which learns the users' preferences and mobility patterns to recommend POIs. Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommendation by addressing the data sparsity problem. However, existing methods model the geographical influence based on the physical distance between POIs and users, while ignoring the temporal characteristics of such geographical influences. In this paper, we perform a study on the user mobility patterns where we find out that users' check-ins happen around several centers depending on their current temporal state. Next, we propose a spatio-temporal activity-centers algorithm to model users' behavior more accurately. Finally, we demonstrate the effectiveness of our proposed contextual model by incorporating it into the matrix factorization model under two different settings: i) static and ii) temporal. To show the effectiveness of our proposed method, which we refer to as STACP, we conduct experiments on two well-known real-world datasets acquired from Gowalla and Foursquare LBSNs. Experimental results show that the STACP model achieves a statistically significant performance improvement, compared to the state-of-the-art techniques. Also, we demonstrate the effectiveness of capturing geographical and temporal information for modeling users' activity centers and the importance of modeling them jointly.