# Why Users Go There: World Knowledge-Augmented Generative Next POI Recommendation

arXiv: https://arxiv.org/abs/2605.11807 · HTML: https://mlsanity.com/paper/2605.11807/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 12 May 2026.

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

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [RecMind: Large Language Model Powered Agent For Recommendation](https://mlsanity.com/paper/2308.14296/) (2023)
- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [Inductive Generative Recommendation via Retrieval-based Speculation](https://mlsanity.com/paper/2410.02939/) (2024)
- [Generative Next POI Recommendation with Semantic ID](https://mlsanity.com/paper/2506.01375/) (2025)
- [CoMaPOI: A Collaborative Multi-Agent Framework for Next POI Prediction Bridging the Gap Between Trajectory and Language](https://mlsanity.com/paper/2505.23837/) (2025)
- [Timestamps as Prompts for Geography-Aware Location Recommendation](https://mlsanity.com/paper/2304.04151/) (2023)
- [LLM-Aligned Geographic Item Tokenization for Local-Life Recommendation](https://mlsanity.com/paper/2511.14221/) (2025)
- [Cognitive-Aligned Spatio-Temporal Large Language Models For Next Point-of-Interest Prediction](https://mlsanity.com/paper/2510.14702/) (2025)

## Abstract (arXiv)

Generative point-of-interest (POI) recommendation models based on large language models (LLMs) have shown promising results by formulating next POI prediction as a sequence generation task. However, the knowledge encoded in these models remains fixed after training, making them unable to perceive evolving real-world conditions that shape user mobility decisions, such as local events and cultural trends. To bridge this gap, we propose AWARE (Agent-based World knowledge Augmented REcommendation), which employs an LLM agent to generate location- and time-aware contextual narratives that capture regional cultural characteristics, seasonal trends, and ongoing events relevant to each user. Rather than introducing generic or noisy information, AWARE further anchors these narratives in each user's behavioral context, grounding external world knowledge in personalized spatial-temporal patterns. Extensive experiments on three real-world datasets demonstrate that AWARE consistently outperforms competitive baselines, achieving up to 12.4% relative improvement.