@inproceedings{ding-etal-2026-users,
title = "Why Users Go There: World Knowledge-Augmented Generative Next {POI} Recommendation",
author = "Ding, Qiuyu and
Xu, Heng-Da and
Zhang, Wei and
Lv, Dongyi and
Xia, Changda and
Xiong, Feng and
Xu, Mu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2217/",
pages = "50446--50459",
ISBN = "979-8-89176-459-0",
abstract = "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** (**A**gent-based **W**orld knowledge **A**ugmented **RE**commendation), 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."
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<abstract>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** (**A**gent-based **W**orld knowledge **A**ugmented **RE**commendation), 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.</abstract>
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%0 Conference Proceedings
%T Why Users Go There: World Knowledge-Augmented Generative Next POI Recommendation
%A Ding, Qiuyu
%A Xu, Heng-Da
%A Zhang, Wei
%A Lv, Dongyi
%A Xia, Changda
%A Xiong, Feng
%A Xu, Mu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F ding-etal-2026-users
%X 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** (**A**gent-based **W**orld knowledge **A**ugmented **RE**commendation), 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.
%U https://aclanthology.org/2026.emnlp-main.2217/
%P 50446-50459
Markdown (Informal)
[Why Users Go There: World Knowledge-Augmented Generative Next POI Recommendation](https://aclanthology.org/2026.emnlp-main.2217/) (Ding et al., EMNLP 2026)
ACL