# Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation

arXiv: https://arxiv.org/abs/2201.03450 · HTML: https://mlsanity.com/paper/2201.03450/

## Key facts

- Cited by 1 papers in the map (data through 7 Oct 2026).
- Builds on 9 of its 55 references that are in the map.
- First posted on arXiv 10 Jan 2022.

## Builds on (most cited first, 9 of 9)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)
- [A Critical Study on Data Leakage in Recommender System Offline Evaluation](https://mlsanity.com/paper/2010.11060/) (2020)
- [Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation](https://mlsanity.com/paper/2001.08961/) (2020)
- [Category-Aware Location Embedding for Point-of-Interest Recommendation](https://mlsanity.com/paper/1907.13376/) (2019)
- [LGLMF: Local Geographical based Logistic Matrix Factorization Model for POI Recommendation](https://mlsanity.com/paper/1909.06667/) (2019)
- [Personalized Context-Aware Point of Interest Recommendation](https://mlsanity.com/paper/1806.05736/) (2018)
- [A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendation](https://mlsanity.com/paper/1909.07131/) (2019)
- [A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion](https://mlsanity.com/paper/1807.04210/) (2018)

## Built on by (most cited first, 1 of 1)

- [A Social-aware Gaussian Pre-trained Model for Effective Cold-start Recommendation](https://mlsanity.com/paper/2311.15790/) (2023)

## Abstract (arXiv)

Recommender Systems (RSs) aim to model and predict the user preference while interacting with items, such as Points of Interest (POIs). These systems face several challenges, such as data sparsity, limiting their effectiveness. In this paper, we address this problem by incorporating social, geographical, and temporal information into the Matrix Factorization (MF) technique. To this end, we model social influence based on two factors: similarities between users in terms of common check-ins and the friendships between them. We introduce two levels of friendship based on explicit friendship networks and high check-in overlap between users. We base our friendship algorithm on users' geographical activity centers. The results show that our proposed model outperforms the state-of-the-art on two real-world datasets. More specifically, our ablation study shows that the social model improves the performance of our proposed POI recommendation system by 31% and 14% on the Gowalla and Yelp datasets in terms of Precision@10, respectively.