Enhancing Geospatial Recommendations through Natural Language Processing with Large Language Models

With the success of large language models (LLMs) such as OpenAI’s ChatGPT, Meta’s LLama, and Google’s Gemmini, there is a growing trend to leverage natural language queries for database processing. For example, Chroma DB stores documents in an embedded representation, allowing it to find data that closely matches the embeddings of user queries. However, the application of this technology to geospatial data, such as places of interest on a map, remains underexplored. This paper presents an exemplary application of place recommendation by utilizing the user’s location to crawl and filter a few recommended results, thereby finding the most relevant place based on the user’s natural-language-based query. This approach offers new insights for enhancing location-based services, providing a framework for more contextually relevant and personalized user experiences.

In 2024 Korea Computer Congress (KCC 2024)