# LGLMF: Local Geographical based Logistic Matrix Factorization Model for POI Recommendation

arXiv: https://arxiv.org/abs/1909.06667 · HTML: https://mlsanity.com/paper/1909.06667/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction](https://mlsanity.com/paper/2110.01401/) (2021)
- [A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security](https://mlsanity.com/paper/2410.02191/) (2024)
- [The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation](https://mlsanity.com/paper/2202.13307/) (2022)
- [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)
- [Recommending POIs for Tourists by User Behavior Modeling and Pseudo-Rating](https://mlsanity.com/paper/2110.06523/) (2021)

## Abstract (arXiv)

With the rapid growth of Location-Based Social Networks, personalized Points of Interest (POIs) recommendation has become a critical task to help users explore their surroundings. Due to the scarcity of check-in data, the availability of geographical information offers an opportunity to improve the accuracy of POI recommendation. Moreover, matrix factorization methods provide effective models which can be used in POI recommendation. However, there are two main challenges which should be addressed to improve the performance of POI recommendation methods. First, leveraging geographical information to capture both the user's personal, geographic profile and a location's geographic popularity. Second, incorporating the geographical model into the matrix factorization approaches. To address these problems, a POI recommendation method is proposed in this paper based on a Local Geographical Model, which considers both users' and locations' points of view. To this end, an effective geographical model is proposed by considering the user's main region of activity and the relevance of each location within that region. Then, the proposed local geographical model is fused into the Logistic Matrix Factorization to improve the accuracy of POI recommendation. Experimental results on two well-known datasets demonstrate that the proposed approach outperforms other state-of-the-art POI recommendation methods.