# Plugging Small Models in Large Language Models for POI Recommendation in Smart Tourism
## 1 Introduction  
As cities are constantly developing and innovating, smart tourism--a crucial part of smart cities--is gaining more attention. With the diversification of tourism options, it uses cutting-edge technology such as IoT, big data, and AI [1]. Its aim is to connect and manage urban areas and tourism resources in a smart way. In this way, it offers personalized services to tourists, making tourism more convenient and satisfying [2]. In particular, the rise of location-based social networks (LBSNs) such as Yelp ([[https://www.yelp.com/](https://www.yelp.com/)]([https://www.yelp.com/](https://www.yelp.com/))) and Foursquare ([[https://foursquare.com/](https://foursquare.com/)]([https://foursquare.com/](https://foursquare.com/))) has led to explosive growth in spatiotemporal data. These rich data present great opportunities for the advancement of point-of-interest (POI) recommendation, an important task in smart tourism. POI recommendation aims to recommend POIs that users may be interested in according to their historical data, so as to achieve a more user-friendly smart tourism service. Recent advances [3] in POI recommendation are shifting from classical neural network models to new LLM-based frameworks. In addition to early methods [4], existing mainstream classic models based on deep learning mainly have two directions. Firstly, sequence-based approaches are harnessedfor trajectory modeling. Secondly, graph-based methods for learning complex relations. Moreover, a handful of recent studies [5; 6] have begun to shift their focus to mining time information to capture time slot preferences. Some studies [6; 7] have further introduced target time to provide users with recommendations for specific time slots, which better meet their daily needs. Recently, LLMs have attracted significant attention. Existing research [8; 9; 10] has proven that LLMs can acquire and represent spatiotemporal knowledge from training corpora, as well as exhibit strong capabilities for time series prediction, thus laying a theoretical foundation for the next POI recommendation. [PERSON] et al. [11] have made preliminary attempts. They primarily rely on users' check-in history records to execute zero-shot prompting. Based on extensive common sense [12; 13], LLMs can perform robust predictions even for cold-start users. Moreover, LLMs can provide corresponding explanations while predicting, thus addressing the issue of poor interpretability in classic DL-based methods [14]. However, the aforementioned existing research still has some limitations. First, unlike LLMs that possess a lot of common sense, classic models need to extend proprietary modules to solve specific problems. For example, users frequently check in various affordable stores, we can realize that users have poor economic ability and recommend affordable POIs, while classic models need to expand an economic preference module. More critically, classic models lack the ability to directly explain their predictions, which limits their interpretability and practicality in real-world applications. Second, LLMs' limited context window hinders their ability to process complete trajectory datasets simultaneously, restricting comprehensive analysis of macro-level user behavior patterns. And it also introduces an important issue, that is, due to the random sampling and extraction of partial trajectories, the target POI may not appear in the current context. Moreover, many LLM-based models perform only simple in-context learning (ICL). Therefore, although LLM-based models have extensive common sense and can eliminate many unreasonable candidate POIs, it is difficult to fully capture the various fine-grained preferences of users. To address the aforementioned limitations, we propose PSLM4 ST, a novel framework that Plugs Small Models into Large Language Models for POI recommendation in Smart Tourism.