# Point of Interest Recommendation: Pitfalls and Viable Solutions

arXiv: https://arxiv.org/abs/2507.13725 · HTML: https://mlsanity.com/paper/2507.13725/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 167 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Builds on (most cited first, 27 of 27)

- [YFCC100M: The New Data in Multimedia Research](https://mlsanity.com/paper/1503.01817/) (2015)
- [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020)
- [A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research](https://mlsanity.com/paper/1911.07698/) (2019)
- [Exploring Data Splitting Strategies for the Evaluation of Recommendation Models](https://mlsanity.com/paper/2007.13237/) (2020)
- [A Critical Study on Data Leakage in Recommender System Offline Evaluation](https://mlsanity.com/paper/2010.11060/) (2020)
- [Personalized Showcases: Generating Multi-Modal Explanations for Recommendations](https://mlsanity.com/paper/2207.00422/) (2022)
- [WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?](https://mlsanity.com/paper/2305.06415/) (2023)
- [Trustworthy Recommender Systems](https://mlsanity.com/paper/2208.06265/) (2022)
- [Multi-User Chat Assistant (MUCA): a Framework Using LLMs to Facilitate Group Conversations](https://mlsanity.com/paper/2401.04883/) (2024)
- [Learning Points and Routes to Recommend Trajectories](https://mlsanity.com/paper/1608.07051/) (2016)
- [Point-of-Interest Recommender Systems based on Location-Based Social Networks: A Survey from an Experimental Perspective](https://mlsanity.com/paper/2106.10069/) (2021)
- [Recommendation with Generative Models](https://mlsanity.com/paper/2409.15173/) (2024)
- [Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation](https://mlsanity.com/paper/2001.08961/) (2020)
- [SynthTRIPs: A Knowledge-Grounded Framework for Benchmark Query Generation for Personalized Tourism Recommenders](https://mlsanity.com/paper/2504.09277/) (2025)
- [Improving Accountability in Recommender Systems Research Through Reproducibility](https://mlsanity.com/paper/2102.00482/) (2021)
- [COVID-19 is linked to changes in the time-space dimension of human mobility](https://mlsanity.com/paper/2201.06527/) (2022)
- [Towards Individual and Multistakeholder Fairness in Tourism Recommender Systems](https://mlsanity.com/paper/2309.02052/) (2023)
- [Analyzing 'Near Me' Services: Potential for Exposure Bias in Location-based Retrieval](https://mlsanity.com/paper/2011.07359/) (2020)
- [Jane Jacobs in the Sky: Predicting Urban Vitality with Open Satellite Data](https://mlsanity.com/paper/2102.00848/) (2021)
- [Biases in human mobility data impact epidemic modeling](https://mlsanity.com/paper/2112.12521/) (2021)
- [A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data](https://mlsanity.com/paper/2308.07426/) (2023)
- [Privacy and the City: User Identification and Location Semantics in Location-Based Social Networks](https://mlsanity.com/paper/1503.06499/) (2015)
- [Widening the Role of Group Recommender Systems with CAJO](https://mlsanity.com/paper/2504.05934/) (2025)
- [Predicting Group Choices from Group Profiles](https://mlsanity.com/paper/2308.03083/) (2023)
- [Ethical and Privacy Considerations with Location Based Data Research](https://mlsanity.com/paper/2403.05558/) (2024)
- [Health-promoting Potential of Parks in 35 Cities Worldwide](https://mlsanity.com/paper/2407.15770/) (2024)
- [The Experience of Running: Recommending Routes Using Sensory Mapping in Urban Environments](https://mlsanity.com/paper/2505.05817/) (2025)

## Abstract (arXiv)

Point of interest (POI) recommendation can play a pivotal role in enriching tourists' experiences by suggesting context-dependent and preference-matching locations and activities, such as restaurants, landmarks, itineraries, and cultural attractions. Unlike some more common recommendation domains (e.g., music and video), POI recommendation is inherently high-stakes: users invest significant time, money, and effort to search, choose, and consume these suggested POIs. Despite the numerous research works in the area, several fundamental issues remain unresolved, hindering the real-world applicability of the proposed approaches. In this paper, we discuss the current status of the POI recommendation problem and the main challenges we have identified. The first contribution of this paper is a critical assessment of the current state of POI recommendation research and the identification of key shortcomings across three main dimensions: datasets, algorithms, and evaluation methodologies. We highlight persistent issues such as the lack of standardized benchmark datasets, flawed assumptions in the problem definition and model design, and inadequate treatment of biases in the user behavior and system performance. The second contribution is a structured research agenda that, starting from the identified issues, introduces important directions for future work related to multistakeholder design, context awareness, data collection, trustworthiness, novel interactions, and real-world evaluation.