# Personalized Context-Aware Point of Interest Recommendation

arXiv: https://arxiv.org/abs/1806.05736 · HTML: https://mlsanity.com/paper/1806.05736/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2018.

## Built on by (most cited first, 4 of 4)

- [Cross-Market Product Recommendation](https://mlsanity.com/paper/2109.05929/) (2021)
- [Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation](https://mlsanity.com/paper/2201.03450/) (2022)
- [Recommending POIs for Tourists by User Behavior Modeling and Pseudo-Rating](https://mlsanity.com/paper/2110.06523/) (2021)
- [AskNearby: An LLM-Based Application for Neighborhood Information Retrieval and Personalized Cognitive-Map Recommendations](https://mlsanity.com/paper/2512.02502/) (2025)

## Abstract (arXiv)

Personalized recommendation of Points of Interest (POIs) plays a key role in satisfying users on Location-Based Social Networks (LBSNs). In this paper, we propose a probabilistic model to find the mapping between user-annotated tags and locations' taste keywords. Furthermore, we introduce a dataset on locations' contextual appropriateness and demonstrate its usefulness in predicting the contextual relevance of locations. We investigate four approaches to use our proposed mapping for addressing the data sparsity problem: one model to reduce the dimensionality of location taste keywords and three models to predict user tags for a new location. Moreover, we present different scores calculated from multiple LBSNs and show how we incorporate new information from the mapping into a POI recommendation approach. Then, the computed scores are integrated using learning to rank techniques. The experiments on two TREC datasets show the effectiveness of our approach, beating state-of-the-art methods.