# A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations

arXiv: https://arxiv.org/abs/2011.10187 · HTML: https://mlsanity.com/paper/2011.10187/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2020.

## Built on by (most cited first, 16 of 16)

- [A Comprehensive Survey on Deep Graph Representation Learning](https://mlsanity.com/paper/2304.05055/) (2023)
- [AutoMLP: Automated MLP for Sequential Recommendations](https://mlsanity.com/paper/2303.06337/) (2023)
- [Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era](https://mlsanity.com/paper/2403.14151/) (2024)
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- [A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security](https://mlsanity.com/paper/2410.02191/) (2024)
- [Self-Supervised Representation Learning for Geospatial Objects: A Survey](https://mlsanity.com/paper/2408.12133/) (2024)
- [Harnessing LLMs for Cross-City OD Flow Prediction](https://mlsanity.com/paper/2409.03937/) (2024)
- [MAS4POI: a Multi-Agents Collaboration System for Next POI Recommendation](https://mlsanity.com/paper/2409.13700/) (2024)
- [RecKG: Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/2501.03598/) (2025)
- [Where have you been? A Study of Privacy Risk for Point-of-Interest Recommendation](https://mlsanity.com/paper/2310.18606/) (2023)
- [Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation](https://mlsanity.com/paper/2311.00491/) (2023)
- [Improving Rating and Relevance with Point-of-Interest Recommender System](https://mlsanity.com/paper/2202.08751/) (2022)
- [PlaceRep: Geospatial Place Representation Learning from Large-Scale Point-of-Interest Data](https://mlsanity.com/paper/2507.02921/) (2025)
- [Recommending POIs for Tourists by User Behavior Modeling and Pseudo-Rating](https://mlsanity.com/paper/2110.06523/) (2021)
- [Causality-Aware Next Location Prediction Framework based on Human Mobility Stratification](https://mlsanity.com/paper/2503.18179/) (2025)
- [From Mobile Data to Business Insights: An End-to-End Analytics Framework for Large-Scale Urban Mobility Analysis and Decision Support](https://mlsanity.com/paper/2607.03394/) (2026)

## Abstract (arXiv)

Location-based Social Networks (LBSNs) enable users to socialize with friends and acquaintances by sharing their check-ins, opinions, photos, and reviews. Huge volume of data generated from LBSNs opens up a new avenue of research that gives birth to a new sub-field of recommendation systems, known as Point-of-Interest (POI) recommendation. A POI recommendation technique essentially exploits users' historical check-ins and other multi-modal information such as POI attributes and friendship network, to recommend the next set of POIs suitable for a user. A plethora of earlier works focused on traditional machine learning techniques by using hand-crafted features from the dataset. With the recent surge of deep learning research, we have witnessed a large variety of POI recommendation works utilizing different deep learning paradigms. These techniques largely vary in problem formulations, proposed techniques, used datasets, and features, etc. To the best of our knowledge, this work is the first comprehensive survey of all major deep learning-based POI recommendation works. Our work categorizes and critically analyzes the recent POI recommendation works based on different deep learning paradigms and other relevant features. This review can be considered a cookbook for researchers or practitioners working in the area of POI recommendation.