Unleashing the Power of Large Language Models for Group POI Recommendations

• URL: http://arxiv.org/abs/2411.13415v1
• Date: Wed, 20 Nov 2024 16:02:14 GMT
• Title: Unleashing the Power of Large Language Models for Group POI Recommendations
• Authors: Jing Long, Liang Qu, Guanhua Ye, Tong Chen, Quoc Viet Hung Nguyen, Hongzhi Yin,
• Abstract summary: Group Point-of-Interest (POI) recommendations aim to predict the next POI that satisfies the diverse preferences of a group of users. Existing methods for group POI recommendations rely on single ID-based features from check-in data. We propose a framework that unleashes power of the Large Language Model (LLM) for context-aware group POI recommendations.
• Score: 39.49785677738477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Group Point-of-Interest (POI) recommendations aim to predict the next POI that satisfies the diverse preferences of a group of users. This task is more challenging than traditional individual POI recommendations due to complex group decision-making and extremely sparse group-level check-in data. Existing methods for group POI recommendations primarily rely on single ID-based features from check-in data, capturing only statistical correlations and failing to fully utilize the rich semantic information contained in the check-ins, resulting in suboptimal performance. To this end, we propose a framework that unleashes the power of the Large Language Model (LLM) for context-aware group POI recommendations (LLMGPR). Our approach first introduces POI tokens alongside the original word tokens of the LLM, which are initialized by applying the LLM to the rich information of each POI. We then propose a novel sequencing adapter guided by Quantized Low-Rank Adaptation (QLORA) to modify the LLM. The enhanced LLM can learn sequence representations by combining semantic-enhanced POI tokens and rich contextual information including positional encodings and spatio-temporal differences. This approach can be adapted for learning either group or user representations depending on the sequence type. Furthermore, we enhance group representations by aggregating individual member representations with another QLORA-based aggregation adapter and introducing a self-supervised learning task that predicts the purpose of check-in sequences, alleviating the data sparsity issue. Our experimental results demonstrate that LLMGPR outperforms existing methods, effectively addressing group-level data sparsity and providing superior recommendations.

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