# Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization

arXiv: https://arxiv.org/abs/2003.05610 · HTML: https://mlsanity.com/paper/2003.05610/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2020.

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

- [Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation](https://mlsanity.com/paper/2202.04893/) (2022)
- [Semi-decentralized Federated Ego Graph Learning for Recommendation](https://mlsanity.com/paper/2302.10900/) (2023)
- [Decentralized Collaborative Learning Framework for Next POI Recommendation](https://mlsanity.com/paper/2204.06516/) (2022)
- [A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security](https://mlsanity.com/paper/2410.02191/) (2024)
- [FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence](https://mlsanity.com/paper/2112.11134/) (2021)
- [Decentralized Graph Neural Network for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2308.08072/) (2023)
- [From Aggregation to Selection: User-Validated Distributed Social Recommendation](https://mlsanity.com/paper/2505.21388/) (2025)
- [Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach](https://mlsanity.com/paper/2204.11602/) (2022)
- [A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems](https://mlsanity.com/paper/2503.12733/) (2025)
- [Federated Latent Factor Model for Bias-Aware Recommendation with Privacy-Preserving](https://mlsanity.com/paper/2504.15090/) (2025)
- [PADER: Paillier-based Secure Decentralized Social Recommendation](https://mlsanity.com/paper/2601.10212/) (2026)

## Abstract (arXiv)

Points of interest (POI) recommendation has been drawn much attention recently due to the increasing popularity of location-based networks, e.g., Foursquare and Yelp. Among the existing approaches to POI recommendation, Matrix Factorization (MF) based techniques have proven to be effective. However, existing MF approaches suffer from two major problems: (1) Expensive computations and storages due to the centralized model training mechanism: the centralized learners have to maintain the whole user-item rating matrix, and potentially huge low rank matrices. (2) Privacy issues: the users' preferences are at risk of leaking to malicious attackers via the centralized learner. To solve these, we present a Decentralized MF (DMF) framework for POI recommendation. Specifically, instead of maintaining all the low rank matrices and sensitive rating data for training, we propose a random walk based decentralized training technique to train MF models on each user's end, e.g., cell phone and Pad. By doing so, the ratings of each user are still kept on one's own hand, and moreover, decentralized learning can be taken as distributed learning with multi-learners (users), and thus alleviates the computation and storage issue. Experimental results on two real-world datasets demonstrate that, comparing with the classic and state-of-the-art latent factor models, DMF significantly improvements the recommendation performance in terms of precision and recall.