Joeun Kim, Seunghyouk Yoon, Xuan-Bach Le, Youngeun Nam, Doyoung Kim, Hwanjun Song, Jae-Gil Lee. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Abstract Point-of-interest (POI) recommendation systems play an important role in various location-based services by improving the user experience. Previous research has leveraged large-scale visit records to predict a user’s next visit POI based on the behavior of similar users. However, with the increasing emphasis on privacy preservation, there is a shift towards zero-shot recommendation that does not require training and only uses individual visit history data. As a better alternative to traditional zero-shot recommender systems, this paper proposes a novel zero-shot recommender system leveraging the ability of pre-trained large language models (LLMs) to understand human behavior called ZeroPOIRec . ZeroPOIRec involves a profiler module that enables LLMs to extract individual user preferences from multiple aspects, including spatio-temporal patterns and individual characteristics, and a recommender module that enhances the zero-shot POI recommendation performance via candidate refinement and prioritization. Through experiments using a benchmark dataset and a newly introduced real-world dataset with semantic variables, we demonstrate that, despite ZeroPOIRec being a zero-shot approach, it outperforms state-of-the-art methods in terms of recommendation performance.