Large Language Models for Next Point-of-Interest Recommendation

TL;DR

This paper introduces a novel framework leveraging pretrained Large Language Models to improve next POI recommendation by effectively utilizing rich contextual information from Location-Based Social Network data, outperforming existing models.

Contribution

The paper presents a new LLM-based framework that preserves and comprehends heterogeneous LBSN data, addressing limitations of previous numerical-only approaches.

Findings

Outperforms state-of-the-art models on three real-world datasets.

Effectively uses contextual information to improve recommendation accuracy.

Alleviates cold-start and short trajectory problems.

Abstract

The next Point of Interest (POI) recommendation task is to predict users' immediate next POI visit given their historical data. Location-Based Social Network (LBSN) data, which is often used for the next POI recommendation task, comes with challenges. One frequently disregarded challenge is how to effectively use the abundant contextual information present in LBSN data. Previous methods are limited by their numerical nature and fail to address this challenge. In this paper, we propose a framework that uses pretrained Large Language Models (LLMs) to tackle this challenge. Our framework allows us to preserve heterogeneous LBSN data in its original format, hence avoiding the loss of contextual information. Furthermore, our framework is capable of comprehending the inherent meaning of contextual information due to the inclusion of commonsense knowledge. In experiments, we test our framework on three real-world LBSN datasets. Our results show that the proposed framework outperforms the state-of-the-art models in all three datasets. Our analysis demonstrates the effectiveness of the proposed framework in using contextual information as well as alleviating the commonly encountered cold-start and short trajectory problems.