Open-Set Living Need Prediction with Large Language Models

TL;DR

This paper introduces PIGEON, a system using large language models to predict diverse living needs in an open-set manner, significantly improving recall over traditional closed-set methods for personalized life service recommendations.

Contribution

It redefines living need prediction as an open-set problem and leverages LLMs with a novel retrieval and alignment approach, enabling more flexible and accurate predictions.

Findings

PIGEON outperforms closed-set methods with 19.37% higher recall.

Human evaluation confirms the reasonableness of predictions.

Instruction tuning allows smaller LLMs to perform competitively.

Abstract

Living needs are the needs people generate in their daily lives for survival and well-being. On life service platforms like Meituan, user purchases are driven by living needs, making accurate living need predictions crucial for personalized service recommendations. Traditional approaches treat this prediction as a closed-set classification problem, severely limiting their ability to capture the diversity and complexity of living needs. In this work, we redefine living need prediction as an open-set classification problem and propose PIGEON, a novel system leveraging large language models (LLMs) for unrestricted need prediction. PIGEON first employs a behavior-aware record retriever to help LLMs understand user preferences, then incorporates Maslow's hierarchy of needs to align predictions with human living needs. For evaluation and application, we design a recall module based on a fine-tuned text embedding model that links flexible need descriptions to appropriate life services. Extensive experiments on real-world datasets demonstrate that PIGEON significantly outperforms closed-set approaches on need-based life service recall by an average of 19.37%. Human evaluation validates the reasonableness and specificity of our predictions. Additionally, we employ instruction tuning to enable smaller LLMs to achieve competitive performance, supporting practical deployment.