Location Recommendation Using Chain-of-Thought Capability in LLMs

 Suggested by: Omid Reza Abbasi, Johannes Scholz and Dominik Kowald (KnowCenter)

Keywords: Location Recommendation, LLMs, Chain-of-Thought

Objective:

1. To investigate the application of LLM reasoning to enhance location-based recommender systems. 

2. To explore the effectiveness of zero-shot CoT prompting for generating relevant location-based recommendations. 

3. To compare the performance of the proposed LLM-based location-based RS approaches against traditional methods. 

Short Description: Recent advancements in artificial intelligence have demonstrated the remarkable capabilities of Large Language Models (LLMs) in performing intricate reasoning tasks. This potential is particularly evident when LLMs are guided by Chain-of-Thought (CoT) prompting. This technique encourages the model to generate a sequence of intermediate reasoning steps to arrive at a final answer. While the efficacy of LLMs and CoT prompting has been notable in domains such as arithmetic and symbolic reasoning, which often involve well-defined logical pathways and verifiable solutions, the application of these techniques within the realm of recommendation systems presents a distinct set of challenges. Recommendation tasks are inherently characterized by subjectivity and the need to cater to individual user preferences, an area where the reasoning mechanisms of LLMs are still being explored. This study aims to investigate the potential of leveraging LLMs' reasoning capabilities to enhance location-based recommender systems. Specifically, we seek to understand how LLMs can reason about geographical context, user mobility, and points of interest to provide more personalized and relevant recommendations. We evaluate the impact of incorporating LLM reasoning in zero-shot scenarios on the quality of location-based recommendations. 

Start: Anytime

Relevant Studies:

Liu, Jiahao, et al. "Enhancing LLM-Based Recommendations Through Personalized Reasoning." arXiv preprint arXiv:2502.13845 (2025). 

Li, Gangmin, Fan Yang, and Yong Yue. "Identify User Intention for Recommendation using Chain-of-Thought Prompting in LLM." Identify User Intention for Recommendation using Chain-of-Thought Prompting in LLM. Springer, 2024.