Open-Set Living Need Prediction with Large Language Models (2025)

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.

## introduction living needs refer to the needs people generate in their daily lives for survival and well-being, from basic needs like food and accommodation to higherlevel needs like socializing and hobbies. living needs play a central role in life service platforms like meituan 1 , which serves as a bridge between millions of life service providers and nearly a billion users @xcite @xcite user time location historical behaviors in similar spatiotemporal contexts going to cocktail bars, eating bbq, having hot pot, going to izakaya, watching movies, playing bowling … 8:00 pm ## problem formulation living need prediction aims to predict a user's living need at a given time and location. this prediction enhances user understanding and supports applications like life service recall. the ground-truth labels for this prediction task are derived from historical platform records, where user reviews sometimes reveal the living needs which drive the consumption. for example, a user might write, "i came here for a quick bite and found it quite good." here, the user's need can be identified as "a quick bite." extracting and clustering such need descriptions, then refining them with expert input, allows us to establish a set of need categories. traditionally, this set remains limited to enable living need classification. formally, each historical consumption instance is represented by a five-tuple (u, t, l, n, s), where u is the user, t the time, l the location foot_0 , n the need type, and s the consumed service. thus, our problem is formulated as follows: gnn encoder user embeddings spatiotemporal context embeddings personal relevant records llm list of life service 1. luxury hotel 2. vegetable salad shop 3. vr experience center 4. aged steakhouse 5. … historical records user time location living need life service 𝒪 + = { 𝑢, 𝑡, 𝑙, 𝑛, 𝑠 } context 𝑐 𝒏 𝟏 𝒏 𝟐 𝒏 𝟒 𝒖 𝟏 𝒏 𝟑 𝒏 𝟏 𝒏 𝟐 𝒏 𝟒 𝒄 𝟏 𝒏 𝟑 𝒏 𝟏 𝒏 𝟐 𝒏 𝟒 𝒄 𝟏 𝒄 𝟐 𝑛-th layer user time location living need spatiotemporal context retrieve 1. at time t, location l, consumed living service s due to the living need n. 2. … user context retrieve similar users' relevant records predict predicted living need maslow's hierarchy guided need refinement llm maslow's hierarchy of needs maslow's hierarchy of living needs 1. physiological needs 1.1 basic diet 1.1.1 home meals 1.1.2 restaurant dining 1.2 … 2. safety needs 3. … llm refined living need refine behavioral embedding learning records retrieval and prediction living needs framework generation need refinement …, i would most likely be looking for a place to socialize and enjoy good food with friends.this time of day in this location seems to be when i typically seek out social experiences combined with dining, particularlyat establishments that offer both good food and a social atmosphere. … based on my physiological need for dining at restaurants and social belonging need for social gatherings, i seek venues that combine dining and socializing. training instance open-set living need prediction predicted living need llm ground-truth living need training query merge ground-truth life service text embedding model finetune fine-tuned text embedding model recall life services training process training samples recall process open-set living-need adaptive life service recall 1. bbq restaurant 2. hotpot restaurant 3. izakaya 4. family style chinese restaurant 5. korean bbq house first, a gnn encoder learns behavioral embeddings for users and spatiotemporal contexts, enabling retrieval of relevant historical records for llm-based need prediction. to refine these predictions, the llm incorporates maslow's theory with platform services, creating a framework that aligns initial predictions with human needs. additionally, a text embedding model is fine-tuned to link flexible need descriptions to relevant life services. to further support online deployment, the initial prompts to llms and their corresponding ground-truth-corrected predictions form instruction tuning pairs, which are used to fine-tune smaller llms. ## methods in this section, we provide a detailed description of each component of our method. figure 2 illustrates our proposed open-set living need prediction system along with the recall method that supports open-set need descriptions. the specific design of each component in our method is explained below. ## experimental results we conducted extensive experiments to address the following research questions: due to space constraints, we present the experimental results for rq6 in the appendix. ## conclusion and future work in this study, we propose pigeon, a novel openset living need prediction system that leverages llms to generate flexible need descriptions unrestricted by pre-defined categories. experimental results demonstrate that pigeon outperforms both traditional closed-set and baseline llm-based methods on need-based life service recall. human evaluation validates the reasonableness and specificity of our generated need descriptions. future work will focus on enhancing pigeon's efficiency for large-scale industrial applications.