Open-Set Living Need Prediction with Large Language Models
Authors: Xiaochong Lan, Jie Feng, Yizhou Sun, Chen Gao, Jiahuan Lei, Xinleishi, Hengliang Luo, Yong Li
Accepted at: acl25

Open-Set Living Need Prediction with Large Language Models

Xiaochong Lan1,2    Jie Feng1*    Yizhou Sun1    Chen Gao1
Jiahuan Lei2    Xinlei Shi2    Hengliang Luo2    Yong Li1*
1Department of Electronic Engineering, BNRist, Tsinghua University   2Meituan
lanxc22@mails.tsinghua.edu.cn   fengjie@tsinghua.edu.cn   liyong07@tsinghua.edu.cn

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.

1 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 higher-level needs like socializing and hobbies. Living needs play a central role in life service platforms like Meituan1, which serves as a bridge between millions of life service providers and nearly a billion users (Chen et al., 2022; Liu et al., 2022, 2025;

Figure 1: We define living need prediction as an open-set classification problem with unlimited classes to handle the complexity of living needs.

Liang et al., 2024). On such platforms, user purchases are fundamentally driven by their underlying living needs - for example, when users order food delivery, it is typically motivated by their need for food. Therefore, accurate prediction of users’ living needs is crucial for service recommendation, as it enables platforms to suggest services that directly address these needs, thereby improving recommendation accuracy and user experience.

Living need prediction aims to predict a user’s needs based on their spatiotemporal context. Previous studies have treated this as a closed-set classification problem, where models select from limited predefined need categories (Ping et al., 2021; Lan et al., 2023). However, this closed-set formulation has inherent limitations. First, users’ needs are highly diverse, making it impossible to list all possibilities. Additionally, users’ needs may be vague, such as seeking a relaxing atmosphere rather than specific venue types like restaurants or bars. Furthermore, users’ needs can be composite, such as wanting a filling while also healthy meal. Obviously, predicting living needs within a fixed set of categories cannot handle these complexities.

In this work, we reformulate living need prediction as an open-set classification problem with unlimited classes, moving beyond the constraints of predefined categories (Zhang et al., 2021; Bao et al., 2022). Following insights that generative models show promise for open-set classification (Mundt et al., 2019) and building on recent advances (Zareian et al., 2021; Pham et al., 2024),
we leverage large language models (LLMs) for this task. LLMs can generate unrestricted textual descriptions of living needs, naturally addressing the limitations of closed-set methods. Their extensive common sense knowledge enables deeper understanding of complex human needs. Through this approach, we aim to achieve truly open-set need prediction capable of identifying an unlimited range of living needs.

However, using LLMs for open-set living need prediction introduces additional challenges:
• First, LLMs lack understanding of individual user preference. While LLMs hold extensive general knowledge, they lack understanding of specific user preferences. To bridge this gap, we must provide LLMs with relevant user behavior data. However, achieving optimal prediction accuracy requires careful selection and providing an appropriate number of highly relevant historical records (Lin et al., 2024), making the design of effective retrieval methods crucial.
• Second, LLMs lack explicit knowledge of structured human living needs. While LLMs naturally support flexible need predictions, they sometimes produce descriptions that deviate from needs, like overly specific actions (e.g., “eat a KFC burger”). Aligning predictions with a structured living needs framework is challenging.
• Third, applying flexible need predictions in downstream tasks is difficult. The most typical application of need prediction is to recall life services that can fulfill a need. However, mapping a vast range of need types to the appropriate services is highly complex, making it challenging to model these associations effectively.

To tackle these challenges, we introduce our system, PIGEON (short for oPen set liVing nEeds predictiON). For the first challenge, unlike traditional text embedding-based retrieval methods, we design a historical interactions-based encoder to help retrieve the most relevant historical information for the LLM to understand individual preferences. To address the second challenge, we guide the LLM to use Maslow’s theory to organize platform services, creating a structured framework of users’ living needs. The LLM then uses this structure to refine its initial predictions. For the third challenge, we develop a fine-tuning approach for a text embedding model, creating a recall module capable of understanding complex relationships between flexible living needs and vast life services. Additionally, to make our method deployable on platforms, we design an instruction tuning approach that enables smaller LLMs to achieve competitive performance.

We compare our method with closed-set classification approaches and LLM-based baselines on recalling appropriate life services, and the experimental results demonstrate the effectiveness of our approach. Human evaluation confirms that our method generates predictions with superior reasonableness and specificity compared to closed-set methods. Cases illustrate that our method can accurately and comprehensively predict users’ needs, handling vague and composite living needs. Ablation studies further validate the contributions of each component in our design.

In summary, the main contributions of our work are as follows:
• To the best of our knowledge, we are the first to explore the open-set living need prediction problem, which is a significant yet under-addressed challenge on life services platforms.
• We propose PIGEON, an LLM-based open-set living need prediction system, which achieves state-of-the-art performance.
• We design a recall approach for open-set needs and a fine-tuning method that enables smaller LLMs to achieve competitive performance, supporting the practical deployment of our method in online environments.

2 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\(^2\), \(n\) the need type, and \(s\) the consumed service. Thus, our problem is formulated as follows:
• Input: \(O^+ = \{(u, t, l, n, s) \mid u \in U, t \in T, l \in L, n \in N, s \in S\}\), where \(U, T, L, N,\) and \(S\)

2In this work, location is in the form of functional zones.
Figure 2: Architecture of our PIGEON system for open-set living need prediction and supporting recall module. 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.

denote the sets of users, time points, locations, need types, and life services, respectively.
• Output: A model that, given \( u, t, \) and \( l, \) can infer the user’s type of living need as an unrestricted need description, covering limitless types.

3 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.

3.1 Behavior Records Retrieval Supported Living Need Prediction

We aim to achieve open-set living need prediction with LLMs. Although LLMs possess extensive common sense, they lack specific knowledge of individual users. While explicit user profiles could provide such knowledge, they are difficult to obtain as they rely on users’ active input. Instead, we leverage behavioral data that is naturally generated through user interactions. We augment LLM prediction with in-context learning from retrieved historical records (Luo et al., 2024; Wang and Lim, 2024), to help the LLM obtain knowledge about the specific user. Instead of traditional text embedding-based retrieval which only captures general text-level similarity, we propose behavioral embeddings learned from users’ consumption histories. These embeddings directly encode individual preferences and contextual effects, enabling more precise retrieval of records for personalized predictions.

Behavioral Embedding Learning  To encode the multiple relationships among users, spatiotemporal contexts, and needs, we use graph neural network (GNN) for embedding learning. First, we construct the graph. There are three types of nodes in the graph: user nodes \( u, \) spatiotemporal context nodes \( c, \) and living need nodes \( n. \) Since time and location jointly influence users’ living needs, we combine them into unified spatiotemporal context nodes \( c. \) The graph has two types of weighted edges: user-need edges \( (u, n) \) and context-need edges \( (c, n). \) For each consumption record where user \( u \) generates need \( n \) in context \( c, \) we increment the weights of both \( (u, n) \) and \( (c, n) \) by 1.

Next, we describe the representation learning method on the graph. We first randomly initialize embeddings \( e_u^{(0)}, e_c^{(0)}, \) and \( e_n^{(0)} \) for users, contexts, and needs respectively. For all kinds of nodes, the propagation on the graph is as follows:

\[
e_i^{(k+1)} = \sum_{j \in \mathcal{N}(i)} \frac{w_{ij}}{D_i \cdot D_j} e_j^{(k)},
\]

where \( e_i^{(k)} \) is node \( i \)'s embedding at layer \( k, w_{ij} \) is the weight of edge \( (i, j), \) and \( \mathcal{N}(i) \) denotes the set of neighboring nodes of \( i. \) \( D_i = \sqrt{\sum_{j' \in \mathcal{N}(i)} w_{ij'}} \) and \( D_j = \sqrt{\sum_{i' \in \mathcal{N}(j)} w_{i'j}} \) are normalization factors. After \( K \) propagation layers, we obtain the final embedding through layer combination and normalization:

\[
e_i = \frac{\sum_{k=0}^K e_i^{(k)}}{\left| \sum_{k=0}^K e_i^{(k)} \right|}
\]
To train the GNN encoder, we construct a Bayesian Personalized Ranking (BPR) loss:

\[
\mathcal{L}_{\text{BPR}} = - \sum_{(u, c, n, n') \in \mathcal{D}} \ln \sigma \left( \mathbf{e}_{uc}^\top \mathbf{e}_n - \mathbf{e}_{uc}^\top \mathbf{e}_{n'} \right),
\]

where \( \sigma \) is the sigmoid function, \( \mathbf{e}_{uc} = \mathbf{e}_u + \mathbf{e}_c \), and \( \mathcal{D} \) represents the set of training instances \((u, c, n, n')\), with \(n\) as the positive need generated by user \(u\) in context \(c\) and \(n'\) a randomly sampled negative need. Finally, after supervised training on historical records, we obtain embeddings for users and spatiotemporal contexts that effectively capture user preferences and contextual influences.

Records Retrieval and Prediction Next, we utilize an LLM to predict the living need of a user in a given spatiotemporal context, based on retrieved most relevant behavioral records. While a user’s own historical records are essential for personalization, relying solely on individual history has limitations: users may have unexplored needs that they haven’t yet acted upon, or they may be in novel situations where relevant personal records are limited. To address these limitations, we complement individual records with historical data from users who exhibit similar behavioral patterns.

For user \(u\) in context \(c\), we first retrieve personal relevant records. From the user’s entire historical record, we select the \(K_p\) historical instances with context embeddings \(e_{c,i}\) most similar to the current context embedding \(e_c\). For the relevant historical records of similar users, we compute \(e_{uc} = e_u + e_c\) and retrieve \(K_s\) historical records with embeddings \(e_{uc,i}\) most similar to the current \(e_{uc}\), from all history records. The similarity is measured using cosine similarity, for example,

\[
\operatorname{sim}(e_{c,i}, e_c) = \frac{e_{c,i}^\top e_c}{\|e_{c,i}\|\|e_c\|}.
\]

Using the retrieved historical records, we then employ the LLM to predict the user’s needs in the current spatiotemporal context. The specific prompt is as follows:

You are a user on a life service platform. At [time] in [location], what kind of living need are you most likely to have?
1. Your following past behaviors are provided for reference: [Personal Relevant Records]
2. Behaviors of other users in similar contexts are: [Similar Users’ Relevant Records]
Considering the current time, location, and preferences indicated by previous consumption behaviors, please infer and describe your potential living needs.

Here, the personal relevant records and similar users’ relevant records are provided to the LLM in textual form, including information on time, location, type of living need, and the consumed life service. For example: “At 5 PM at home, you generated the living need for a quick meal and ordered a hamburger delivery service.” Using this prompt as input, the LLM outputs a prediction of living needs expressed in an unrestricted text format.

3.2 Maslow’s Hierarchy Guided Living Need Refinement

Now, we have obtained a flexible text-based prediction of living needs. While the LLM allows for flexible predictions, some outputs may not align well with living needs. For instance, certain predictions may focus excessively on specific actions rather than genuine needs, such as “eating a McDonald’s burger”, or suggest needs unsupported by life service platforms, like “writing emails”. Since living needs on service platforms naturally range from basic physiological needs to higher-level social and self-development needs, we leverage the widely-accepted Maslow’s hierarchy (McLeod, 2007) as a theoretical foundation to guide the refinement of these predictions. Specifically, we use the LLM to integrate Maslow’s hierarchy with the platform’s service offerings, creating a structured framework that helps align predictions with genuine human living needs while preserving their flexibility. In Section A.8, we further explain the rationale for using Maslow’s theory.

To construct this framework, we prompt the LLM with a list of available life services:

[Life Services List]
These are life services on a platform that can meet human living needs. Based on this list, along with Maslow’s hierarchy of needs, please generate a three-tiered framework of human living needs, ensuring that each need can be fulfilled by the listed services.

For large-scale service lists in the production environment that exceed LLM’s token limit, we either categorize services first or process them in sequential batches. Using the generated framework, we then refine the initial predictions:

You are a user on a life service platform. An inference about your current living need at [time] in [location] is [initial prediction].
Please use the following human living needs framework to further refine this inference, making it align with the framework’s scope. Your response should be concise and as informative as possible, around 20 words.
[Human living needs framework]

3.3 Open-Set Living Need-Adaptive Life Service Recall

In practical applications, predicted needs are typically used as one source in the recall stage to identify suitable life services as candidate recommendations. For closed-set methods, associations between needs and services rely on manually set rules. However, the flexibility of LLM-generated needs poses challenges for recall, as managing the complex mapping between a limitless range of needs and the services that fulfill them is nearly impossible. To address this, we design a recall approach based on a fine-tuned text embedding model, leveraging the commonsense knowledge embedded within language models to enhance recall accuracy.

In our recall method, need descriptions serve as queries to identify relevant services within the whole corpus of services. To fine-tune the text embedding model, we construct training samples from historical data by pairing actual needs with the ordered life services as ground truth. To adapt the model to flexible need descriptions as queries, we generate flexible living needs predictions for historical records and then use the LLM to refine these predictions based on the ground-truth closed-set living needs \( n \). The specific prompt is:

The ground-truth living need is [living need]; a flexible need description is: [predicted need]. Based on the ground-truth living need, please revise the flexible need description to maintain flexibility without compromising accuracy. Provide only the revised flexible need description, approximately 20 words.

Now we obtain flexible yet accurate need descriptions to serve as training queries for recall. Each refined need description \( q \) is encoded as a vector embedding \( f(q) \) using a pretrained transformer-based text embedding model \( f(\cdot) \). The training process optimizes the model with a triplet loss:

\[
\mathcal{L} = \sum_{(q, s, s') \in \mathcal{D}} \max \left(0, \operatorname{sim}(q, s') - \operatorname{sim}(q, s) + \alpha\right)
\]

where \( \operatorname{sim}(q, s) = \frac{f(q)^T f(s)}{\|f(q)\|\|f(s)\|} \) is the cosine similarity between the query \( q \) and the positive life service \( s \) (the actual consumed service), \( s' \) is a randomly selected negative service, and \( \alpha \) is a margin. This loss encourages the model to embed services that can fulfill the need closer to the query than those that cannot fulfill the need, enhancing recall precision. During inference, we use the fine-tuned model \( f_{\text{finetune}}(\cdot) \) to encode both queries and services, recalling services with highest similarities.

3.4 Domain Adaptation for LLMs

Practical deployment of our method faces computational and latency constraints, requiring high performance under limited computing resources and low latency for online deployment. In our current framework, LLM inference represents the primary computational and latency bottleneck. Therefore, we employ instruction tuning to enhance the capabilities of smaller LLMs that have lower computational requirements and latency. Our method naturally defines input-output pairs: the input being the initial prompt containing spatiotemporal context and user history, and the output being the refined training query that balances both accuracy and flexibility. After processing numerous prediction tasks with high-performance LLMs, we naturally accumulate these pairs as instruction tuning data. Using this constructed training data, we fine-tune smaller LLMs. Notably, we adopt an end-to-end training approach, aiming to train LLMs that can achieve good results in a single inference step using data generated through the two-step framework. We implement full-parameter fine-tuning using accelerate (Gugger et al., 2022).

4 Experimental Setup

4.1 Datasets

We collect two large-scale datasets from the Meituan platform, each containing 120 days of user consumption records from a different city in China. These records include user details, timestamps, locations, actual life service consumption, and living needs extracted from reviews. In these datasets, time is categorized into 48 intervals of half-hour windows, and locations are grouped into 13 categories based on functionality (e.g., residential areas, commercial streets). Following (Li et al., 2022b), we partition the datasets by time, using the first 96 days for training, the next 12 days for validation, and the final 12 days for testing. Dataset
<table>
  <tr>
    <th>Dataset</th>
    <th>#Records</th>
    <th>#Users</th>
    <th>#Locs</th>
    <th>#Time</th>
    <th>#Needs</th>
    <th>#Services</th>
  </tr>
  <tr>
    <td>Beijing</td>
    <td>6,942,123</td>
    <td>37,226</td>
    <td>14</td>
    <td>48</td>
    <td>20</td>
    <td>2026</td>
  </tr>
  <tr>
    <td>Shanghai</td>
    <td>7,797,225</td>
    <td>43,213</td>
    <td>14</td>
    <td>48</td>
    <td>20</td>
    <td>2155</td>
  </tr>
</table>

Table 1: Statistics of our utilized datasets.

statistics are provided in Table 1.

4.2 Implementation Details

In our main experiments, we primarily use GPT-4o mini (OpenAI, 2024) as the LLM backbone for our method, chosen for its ease of use, relatively high performance, and cost-effectiveness. The cost is around $1.05 for 10,000 times of predictions. To maximize reproducibility, we set the temperature parameter to 0. We also report performance with other LLMs as backbones. For the text embedding model used as the recall module, we adopt bge-base-v1.5 (Xiao et al., 2023). We implement the GNN encoder using the PyTorch (Paszke et al., 2019) framework. We implement graph propagation efficiently using sparse matrix operations. Each reported result is the average of 5 runs.3

4.3 Evaluation Metric

Evaluating flexible need predictions poses inherent measurement challenges due to their open-ended nature. We address this by assessing prediction quality through the downstream task of life service recall, where need predictions serve as queries to retrieve relevant services. For quantitative evaluation, we adopt two widely used metrics: Recall@K and NDCG@K. Recall@K is defined as the proportion of actually consumed services that appear in the top K recalled results. NDCG@K further emphasizes that ground-truth services should be ranked high in the recalled list. While real-world recall systems focus less on NDCG, we include it here to better evaluate our recall system’s ability to identify services that precisely meet user needs.

4.4 Comparison Methods

For life service recall, we compare our method against two types of methods: traditional closed-set living need prediction methods and LLM-based open-set need prediction methods. For closed-set methods, we include industry-standard CTR models: XGBoost (Chen and Guestrin, 2016), DeepFM (Guo et al., 2017), DCN (Wang et al., 2017), XDeepFM (Lian et al., 2018), EulerNet (Tian et al., 2023), which take user, time, and location as input features. We also evaluate graph-based models: LightGCN (He et al., 2020), HAN (Wang et al., 2019), DisenHCN (Li et al., 2022c) that capture complex relationships through graph incorporating user, time, location, need, and service nodes and propagation on the graph. To maximize the recall performance potential of closed-set need prediction methods, we utilize combinations of closed-set needs with time and locations as queries during both the training and prediction phases of the recall model.

For LLM-based open-set methods, we test zero-shot CoT (Kojima et al., 2022), which predicts solely from current context without historical data. We also include two retrieval-enhanced approaches: ReLLa (Lin et al., 2024) and LLMSRec-Syn (Wang and Lim, 2024). Both approaches leverage text embeddings for historical record retrieval, where ReLLa retrieves from individual user records while LLMSRec-Syn additionally incorporates records from users with similar behaviors. For both LLM-based methods, the recall model’s training and usage are consistent with our approach, differing only in flexible need generation. A more detailed description of comparison methods is in Section A.6.

5 Experimental Results

We conducted extensive experiments to address the following research questions:

• RQ1: How is the performance of our open-set need prediction method on life service recall?
• RQ2: How is the quality of living need expressions generated by our method?
• RQ3: Does each component of our method contribute to the final performance in service recall?
• RQ4: How does domain adaptation improve the performance of smaller open-source models?
• RQ5: How does our model perform on different LLM backbones?
• RQ6: How do hyperparameter settings impact the performance of our method?

Due to space constraints, we present the experimental results for RQ6 in the Appendix.

5.1 Recall Performance (RQ1)

We measure the performance of PIGEON and baselines in the life service recall task on two datasets, as shown in Table 2. Key findings are as follows:

• Our method achieves top performance across all metrics on both datasets. On Beijing and
Table 2: Comparison of PIGEON and baselines on the life service recall task. Bold and underlined refer to the best and 2nd best performance. * indicates that PIGEON outperforms the best baseline with \( p < 0.05 \) in the paired t-test.

<table>
  <tr>
    <th rowspan="2">Category</th>
    <th rowspan="2">Model</th>
    <th colspan="4">Beijing</th>
    <th colspan="4">Shanghai</th>
  </tr>
  <tr>
    <th>Recall@10</th>
    <th>Recall@20</th>
    <th>NDCG@10</th>
    <th>NDCG@20</th>
    <th>Recall@10</th>
    <th>Recall@20</th>
    <th>NDCG@10</th>
    <th>NDCG@20</th>
  </tr>
  <tr>
    <td rowspan="7">Closed-Set</td>
    <td>XGBoost</td>
    <td>0.03106</td>
    <td>0.05878</td>
    <td>0.01354</td>
    <td>0.02032</td>
    <td>0.02164</td>
    <td>0.04774</td>
    <td>0.00929</td>
    <td>0.01600</td>
  </tr>
  <tr>
    <td>DeepFM</td>
    <td>0.03378</td>
    <td>0.06486</td>
    <td>0.01546</td>
    <td>0.02319</td>
    <td>0.06047</td>
    <td>0.09230</td>
    <td>0.02903</td>
    <td>0.03710</td>
  </tr>
  <tr>
    <td>DCN</td>
    <td>0.03176</td>
    <td>0.06351</td>
    <td>0.01558</td>
    <td>0.02356</td>
    <td>0.06122</td>
    <td>0.09377</td>
    <td>0.03018</td>
    <td>0.03862</td>
  </tr>
  <tr>
    <td>XDeepFM</td>
    <td>0.03331</td>
    <td>0.06552</td>
    <td>0.01623</td>
    <td>0.02410</td>
    <td>0.06203</td>
    <td>0.09448</td>
    <td>0.03081</td>
    <td>0.03971</td>
  </tr>
  <tr>
    <td>EulerNet</td>
    <td>0.03497</td>
    <td>0.06871</td>
    <td>0.01687</td>
    <td>0.02485</td>
    <td>0.06343</td>
    <td>0.09615</td>
    <td>0.03109</td>
    <td>0.03927</td>
  </tr>
  <tr>
    <td>LightGCN</td>
    <td>0.05510</td>
    <td>0.09410</td>
    <td>0.02624</td>
    <td>0.03594</td>
    <td>0.04375</td>
    <td>0.07701</td>
    <td>0.01939</td>
    <td>0.02785</td>
  </tr>
  <tr>
    <td>HAN</td>
    <td>0.08851</td>
    <td>0.12973</td>
    <td>0.04532</td>
    <td>0.05574</td>
    <td>0.06111</td>
    <td>0.09612</td>
    <td>0.02745</td>
    <td>0.03620</td>
  </tr>
  <tr>
    <td>DisenHCN</td>
    <td>0.09244</td>
    <td>0.13347</td>
    <td>0.04820</td>
    <td>0.05874</td>
    <td>0.07268</td>
    <td>0.12335</td>
    <td>0.03474</td>
    <td>0.04377</td>
  </tr>
  <tr>
    <td rowspan="3">Open-Set</td>
    <td>Zero-shot CoT</td>
    <td>0.03446</td>
    <td>0.05743</td>
    <td>0.01748</td>
    <td>0.02330</td>
    <td>0.02355</td>
    <td>0.04710</td>
    <td>0.01089</td>
    <td>0.01680</td>
  </tr>
  <tr>
    <td>ReLla</td>
    <td>0.05878</td>
    <td>0.10405</td>
    <td>0.03029</td>
    <td>0.04150</td>
    <td>0.05347</td>
    <td>0.09612</td>
    <td>0.02933</td>
    <td>0.03987</td>
  </tr>
  <tr>
    <td>LLMSREC-Syn</td>
    <td>0.08044</td>
    <td>0.12705</td>
    <td>0.04421</td>
    <td>0.06012</td>
    <td>0.06317</td>
    <td>0.10025</td>
    <td>0.03738</td>
    <td>0.04792</td>
  </tr>
  <tr>
    <td>PIGEON</td>
    <td><b>0.10405*</b></td>
    <td><b>0.16622*</b></td>
    <td><b>0.05124*</b></td>
    <td><b>0.06681*</b></td>
    <td><b>0.10503*</b></td>
    <td><b>0.14895*</b></td>
    <td><b>0.04743*</b></td>
    <td><b>0.05845*</b></td>
  </tr>
</table>

Figure 3: Human evaluation results. *** indicates \( p < 0.001 \) in statistical significance test.

Shanghai datasets, our method significantly outperforms the best baseline across all metrics, with relative improvements of 15.18% and 23.55%, respectively. This highlights that our flexible need predictions effectively boost life service recall, leading to more accurate recommendations.

• The approach for historical record retrieval is crucial. Among open-set methods, zero-shot CoT shows the lowest performance, indicating that common sense alone is insufficient for need prediction and that historical data is crucial. ReLla and LLMSREC-Syn, which use text embeddings to retrieve records, also underperform compared to our method, underscoring the effectiveness of our behavior-based representation learning.

• Graph-based approaches lead in closed-set methods. Graph neural networks model complex relationships and cross-influences better than CTR models, which rely on feature interactions. This finding supports the suitability of GNN encoding in our approach.

For fair comparison, LLM outputs are constrained to approximately 10 words. Details of the questionnaire design are in Appendix Section A.4.

Results of human evaluation are shown in Figure 3. Statistical analysis shows that PIGEON generates high-quality predictions across all dimensions. While PIGEON and LLMSREC-Syn achieve comparable rationality scores (4.009 vs. 3.901, \( p = 0.103 \)), PIGEON significantly outperforms in both specificity (3.900 vs. 3.580, \( t = 5.107, p < 0.001 \)) and information density (3.737 vs. 3.413, \( t = 5.062, p < 0.001 \)). Compared to DisenHCN, PIGEON shows substantial improvements across all metrics (\( p < 0.001 \)), with particularly large gains in specificity (\( t = 18.852 \)) and information density (\( t = 13.737 \)).

Notably, LLM-based methods provide richer and more comprehensive need expressions that participants find more specific and actionable, while maintaining high information density despite longer descriptions. In contrast, closed-set predictions, constrained by predefined categories, often appear too generic for specific contexts.

What’s more, case studies in Figure 4 demonstrate how our LLM-based approach addresses closed-set methods’ limitations in capturing diverse, vague, and composite human needs.

In the first case, users ordered Brazilian barbecue, Japanese izakayas, and cocktail bars around 8 PM in commercial street. While closed-set methods categorize this as “good food”, PIGEON recognizes the after-work context and infers a vague need: “relaxation and entertainment after work, to unwind and recharge”.

In the second case, a user ordered food delivery and fresh produce around 12 PM in a residential area with gathering-related comments. PIGEON infers a composite need combining physiological
<table>
  <tr>
    <th colspan="2">Case 1: Vague Living Needs</th>
  </tr>
  <tr>
    <td>Time: 7:00 PM. Location: Commercial Street</td>
    <td></td>
  </tr>
  <tr>
    <td>Personal historical records:</td>
    <td></td>
  </tr>
  <tr>
    <td>• At 10:00 PM, ordered Brazilian BBQ on Commercial Street due to the living need for good food.</td>
    <td></td>
  </tr>
  <tr>
    <td>• At 9:00 PM, ordered Japanese Izakaya on Commercial Street due to the living need for good food.</td>
    <td></td>
  </tr>
  <tr>
    <td>• At 8:00 PM, ordered cocktail bar in Shopping Mall due to the need for a group gathering.</td>
    <td></td>
  </tr>
  <tr>
    <td>Similar users’ historical records:</td>
    <td></td>
  </tr>
  <tr>
    <td>• At 10:30 PM, ordered self-service BBQ on Commercial Street due to the living need for good food.</td>
    <td></td>
  </tr>
  <tr>
    <td>• At 10:30 PM, ordered meal in Shopping Mall due to the living need for having fun.</td>
    <td></td>
  </tr>
  <tr>
    <td>Prediction of PIGEON: Physiological need, leisure and relaxation after work to recover energy.</td>
    <td></td>
  </tr>
  <tr>
    <td>Prediction of DisenHCN: Good food.</td>
    <td></td>
  </tr>
  <tr>
    <th colspan="2">Case 2: Composite Living Needs</th>
  </tr>
  <tr>
    <td>Time: 12:00 PM. Location: Residential Area</td>
    <td></td>
  </tr>
  <tr>
    <td>Personal historical records:</td>
    <td></td>
  </tr>
  <tr>
    <td>• At 11:00 AM, ordered hotpot delivery in the residential area due to the living need for good food.</td>
    <td></td>
  </tr>
  <tr>
    <td>• At 11:30 AM, ordered fresh supermarket delivery in the residential area due to the living need for a group meal.</td>
    <td></td>
  </tr>
  <tr>
    <td>• At 12:00 PM, ordered fresh supermarket delivery in the residential area due to the living need for cooking at home.</td>
    <td></td>
  </tr>
  <tr>
    <td>Similar users’ historical records:</td>
    <td></td>
  </tr>
  <tr>
    <td>• At 12:00 PM, ordered burger delivery in the residential area due to the living need for good food.</td>
    <td></td>
  </tr>
  <tr>
    <td>• At 12:00 PM, ordered fresh supermarket delivery in the residential area due to the living need for cooking at home.</td>
    <td></td>
  </tr>
  <tr>
    <td>Prediction of PIGEON: Physiological need, lunch; Social need, eating with family at home.</td>
    <td></td>
  </tr>
  <tr>
    <td>Prediction of DisenHCN: Cooking at home</td>
    <td></td>
  </tr>
</table>

Figure 4: Cases show that our model can generate flexible living need descriptions covering vague and composite needs, while closed-set classification models cannot.

(eating lunch) and social (family gathering) aspects, while the closed-set model DisenHCN only predicts a single need like “cooking at home”.

These cases show our method effectively handles vague and composite needs, demonstrating clear advantages over closed-set classification methods.

5.3 Ablation Study (RQ3)

To assess each component’s impact on recall performance, we individually remove parts of PIGEON and present results in Table 3 and 4. The key findings are as follows:

• Behavior Records Retrieval. Removing either personal or similar users’ historical record retrieval leads to a marked drop in performance, with both types of records showing a similar degree of impact. What’s more, omitting all historical records causes an even more substantial decrease, highlighting the necessity of using actual behavior data in addition to general LLM common sense.
• Maslow Hierarchy-Guided Need Refinement. Omitting the refinement process and relying only on the LLM’s initial prediction substantially reduces performance, highlighting the importance of aligning predictions with human needs.
• Recall Model Fine-tuning. In our fine-tuning design, to create training queries that are both flexible and accurately aligned with life services, we first generate flexible need descriptions, then refine them using closed-set ground-truth needs from historical data. Our ablation experiments show performance drops when using either only closed-set needs or unrefined flexible needs, while using the original text embedding model without fine-tuning performs even worse. These results validate both our query construction approach and the necessity of fine-tuning for effective service recall.

<table>
  <tr>
    <th>Model</th>
    <th colspan="2">Beijing</th>
  </tr>
  <tr>
    <th></th>
    <th>Recall@10</th>
    <th>Recall@20</th>
  </tr>
  <tr>
    <td>PIGEON</td>
    <td>0.10405</td>
    <td>0.16622</td>
  </tr>
  <tr>
    <td>w/o history record</td>
    <td>0.02770</td>
    <td>0.05743</td>
  </tr>
  <tr>
    <td>w/o personal history</td>
    <td>0.07838</td>
    <td>0.13919</td>
  </tr>
  <tr>
    <td>w/o similar users’ history</td>
    <td>0.07808</td>
    <td>0.13514</td>
  </tr>
  <tr>
    <td>w/o need refinement</td>
    <td>0.09324</td>
    <td>0.14932</td>
  </tr>
  <tr>
    <td>w/o recall model fine-tuning</td>
    <td>0.07095</td>
    <td>0.11486</td>
  </tr>
  <tr>
    <td>w/o flexible needs in queries</td>
    <td>0.07500</td>
    <td>0.12027</td>
  </tr>
  <tr>
    <td>w/o closed-set needs in queries</td>
    <td>0.09403</td>
    <td>0.15176</td>
  </tr>
</table>

Table 3: Results of ablation study on Beijing dataset.

<table>
  <tr>
    <th>Model</th>
    <th colspan="2">Shanghai</th>
  </tr>
  <tr>
    <th></th>
    <th>Recall@10</th>
    <th>Recall@20</th>
  </tr>
  <tr>
    <td>PIGEON</td>
    <td>0.10503</td>
    <td>0.14895</td>
  </tr>
  <tr>
    <td>w/o history record</td>
    <td>0.02673</td>
    <td>0.04838</td>
  </tr>
  <tr>
    <td>w/o personal history</td>
    <td>0.07257</td>
    <td>0.11776</td>
  </tr>
  <tr>
    <td>w/o similar users’ history</td>
    <td>0.06875</td>
    <td>0.11585</td>
  </tr>
  <tr>
    <td>w/o need refinement</td>
    <td>0.09039</td>
    <td>0.14131</td>
  </tr>
  <tr>
    <td>w/o recall model fine-tuning</td>
    <td>0.07257</td>
    <td>0.10948</td>
  </tr>
  <tr>
    <td>w/o flexible needs in queries</td>
    <td>0.07731</td>
    <td>0.11377</td>
  </tr>
  <tr>
    <td>w/o closed-set needs in queries</td>
    <td>0.09322</td>
    <td>0.14438</td>
  </tr>
</table>

Table 4: Results of ablation study on Shanghai dataset.

![Performance (Recall@20) of small LLMs on living need prediction after domain adaptation.](page_1012_1342_370_246.png)

(a) Beijing
(b) Shanghai

Figure 5: Performance (Recall@20) of small LLMs on living need prediction after domain adaptation.

5.4 Domain Adaptation of LLMs (RQ4)

To make our system practically deployable, we design an instruction tuning approach aimed at enabling smaller LLMs to achieve usable performance, thereby reducing computational costs and latency in online deployment. We conduct experiments using Qwen2.5 series models (0.5B, 1.5B, 3B, 7B) as base models, with results shown in Figure 5. We find that through training, the models show significant performance improvements, with 3B models approaching the capabilities of GPT-4o mini, while the 7B model surpasses it. This demonstrates the effectiveness of our instruction tuning method. What’s more, smaller LLMs can achieve competitive results with single-step inference, making our method more feasible for deployment.
Figure 6: The performance of PIGEON and LLMSREC-Syn on various LLM backbones on the Beijing Dataset.

Figure 7: The performance of PIGEON and LLMSREC-Syn on various LLM backbones on the Shanghai dataset.

5.5 Performance on different LLMs (RQ5)

In our experiments, we use GPT-4o mini as the main backbone of PIGEON. In this section, we test PIGEON’s performance with several open-source LLMs to evaluate its effectiveness across these models. We also compare our method with LLMSREC-Syn, which uses a text embedding model to vectorize user and context for record retrieval. The results on the Beijing dataset are shown in Figure 6 and 7, focusing on Recall@20.

The results indicate that even with smaller-scale open-source models, our method performs well, achieving performance comparable to or better than the best closed-set classification models. More importantly, our method outperforms the direct text embedding-based retrieval approach across all open-source models, demonstrating the effectiveness of our design.

6 Related Work

6.1 Open-Set Classification

Open-set classification extends traditional closed-set classification by handling scenarios where the model encounters unseen classes (Scheirer et al., 2014). Early methods used confidence thresholding to reject unfamiliar inputs (Bendale and Boutl, 2016). Recent approaches incorporate generative models (Mundt et al., 2019) and adversarial learning (Zhang et al., 2021; Bao et al., 2022) for enhanced robustness. While recent advances in open-vocabulary object detection have shown promising results in handling unconstrained output spaces through vision-language models (Zareian et al., 2021; Gu et al., 2021; Li et al., 2022a; Pham et al., 2024), extending beyond closed-set formulation remains unexplored for living need prediction. We address this by leveraging LLMs to generate flexible need descriptions without class constraints.

6.2 Domain Applications of LLMs

In recent years, LLMs have flourished and been applied to various domains. Successful application areas include natural sciences (Li et al., 2025), social sciences (Gao et al., 2023, 2024; Li et al., 2023a; Piao et al., 2025; Wang et al., 2024b), urban science (Feng et al., 2025b,d,c), social media analysis (Lan et al., 2024a,b), reinforcement learning (Hao et al., 2025a,b; Xu et al., 2025), literature analysis (Hao et al., 2024), and graph learning (Li et al., 2023b,c; Zhang et al., 2024). However, these methods primarily leverage the world knowledge of LLMs, while the flexible text generation capabilities of LLMs have not been fully utilized and may even become obstacles in structured tasks. In contrast, we effectively leverage both the commonsense and flexible text generation capabilities of LLMs to achieve better human need prediction.

6.3 LLMs for User Modeling

User modeling focuses on understanding users’ behaviors and intentions to provide personalized experiences (Feng et al., 2025a). LLMs have advanced this field through its commonsense knowledge, enabling success across tasks including profiling, recommendation, and behavior detection (Wang et al., 2024a; Liu et al., 2023). However, no existing research has utilized LLMs for predicting user living needs, making our work pioneering in this domain.

7 Conclusion and Future Work

In this study, we propose PIGEON, a novel open-set 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.
8 Limitations

While our approach demonstrates strong performance in open-set living need prediction, several limitations warrant discussion. First, the computational overhead of LLM inference operations poses scalability challenges for large-scale deployment, particularly in real-time scenarios requiring low latency. Second, our evaluation focused primarily on two cities within a single life service platform; broader validation across diverse geographical regions and service platforms would help establish the method’s generalizability. Third, while our approach extends open-set classification beyond traditional boundaries, it may still be constrained by the LLM’s ability to generate contextually appropriate need descriptions. Finally, the quality of predictions may vary depending on the choice of LLM backbone, as shown in our experiments with different models.

9 Ethics Statement

This work involves processing user behavioral data to predict living needs. We ensure all data is anonymized through multiple safeguards: user identifiers are replaced with random codes, locations are aggregated into several functional zones, and timestamps are discretized into 30-minute intervals. We further protect user privacy by removing personal identifiers from review text and filtering out unique behavioral sequences that could enable individual identification. No personally identifiable information is used in the study or retained in our datasets.

The potential risks of behavioral data analysis are significant: malicious actors could potentially use predicted needs to manipulate user choices, create targeted advertising that exploits vulnerabilities, or combine predictions with other data sources to de-anonymize users. Commercial entities might attempt to leverage these predictions for aggressive marketing or price discrimination. There’s also a risk of reinforcing existing biases in user behavior or creating filter bubbles that limit user exposure to diverse services. While we have implemented data obfuscation and anonymization to help reduce these risks, continuous ethical oversight remains necessary in practical applications.

The LLM components of our system utilize the GPT-4o mini API service provided by OpenAI, in full compliance with OpenAI’s terms of service and usage policies. We maintain strict data handling protocols throughout our research to prevent potential privacy breaches or misuse.

10 Acknowledgement

This work was supported in part by the National Key Research and Development Program of China under grant 2024YFC3307603, in part by the China Postdoctoral Science Foundation under grant 2024M761670 and GZB20240384, in part by the Tsinghua University Shuiimu Scholar Program under grant 2023SM235. This work was also supported by Meituan.

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A Appendix

A.1 The Structure of Maslow’s Hierarchy Guided Living Needs Framework

In our method, to ensure that predicted living needs align with actual user needs on life service platforms while maintaining flexibility, we integrate Maslow’s hierarchy of needs with real-world platform services to create a structured framework. Below is the complete framework we developed, which guides our LLM in refining initial predictions to better reflect genuine human needs while preserving their diverse expressions:

• Physiological Needs
  – Basic Diet
    * Home meals
    * Restaurant dining
  – Housing Needs
    * Basic living space
    * Quality living environment
  – Daily Living Supplies
    * Essential supplies
    * Fresh groceries
• Safety Needs
  – Health Protection
    * General wellness maintenance
    * Specialized medical care
    * Preventive healthcare
  – Home Security
    * Property protection
    * Safety maintenance
  – Living Environment
    * Cleaning services
    * Environmental maintenance
• Social Belonging Needs
  – Group Activities
    * Dining and social venues
    * Leisure and entertainment activities
  – Family and Parent-Child Interaction
    * Parent-child activities and entertainment
    * Family celebrations and event organization
• Esteem Needs
  – Knowledge Development
    * Educational training
    * Arts and skill development
  – Career Growth
    * Academic advancement
    * Professional skills
• Self-Actualization Needs
  – Cultural and Artistic Pursuits
    * Cultural experiences and exhibitions
    * Artistic creation and expression
  – Travel and Experiential Activities
    * Travel and sightseeing
    * Outdoor and experiential activities

A.2 Hyperparameter Study (RQ6)

Our method introduces two hyperparameters: \( K_p \), the number of personal relevant history records retrieved, and \( K_s \), the number of relevant history records from similar users. Existing work (Lin et al., 2024) reports that the quantity of historical records provided to the LLM as reference can influence prediction performance. To further investigate the finding in our task, we explore the impact of these two hyperparameters on our model’s performance. The results are shown in Figure 8.

The results indicate that the optimal values for \( K_s \) and \( K_p \) are 5 and 10, respectively, on both datasets. Additionally, we observe that varying \( K_s \) within [1, 2, 5, 10] and \( K_p \) within [2, 5, 10, 20] has minimal effect on model performance. This demonstrates the stability of our method with respect to hyperparameter selection.

A.3 Hyperparameter Settings

In our main method, the hyperparameters are set as follows:

• Number of personal historical records \( K_p \): 5
• Number of similar users’ historical records \( K_s \): 10
• Number of GNN layers: 3
• GNN embedding size: 64
• GNN learning rate: \( 1 \times 10^{-4} \)
• Triplet margin for triplet loss: 0.5
• Learning rate for triplet loss: \( 2 \times 10^{-5} \)

A.4 Details of Human Evaluation (RQ2)

The questionnaire uses a Likert scale format and consists of three groups of questions. Each group evaluates users’ perceptions of need predictions from three models: PIGEON, LLMSREC-Syn, and DisenHCN. Each group contains 7 questions covering the five need categories from physiological to self-actualization needs, with two additional questions addressing common need combinations: physiological+social needs and physiological+safety needs. While PIGEON and LLMSREC-Syn generate predictions of approximately 10 words, DisenHCN provides fixed phrases. We evaluate three dimensions:

• Reasonableness: Assessing whether the predicted need is reasonable given the user’s history and current context.
• Specificity: Evaluating whether the prediction points to needs with concrete fulfillment methods and actionability.
• Information Density: The amount of effective information conveyed per unit of text.
Each dimension is rated on a 5-point Likert scale (1=lowest, 5=highest). To prevent bias, participants are blinded to the source models and only presented with the predicted content. To reduce cognitive load, we present only 5 behavioral records that demonstrate user preferences for each question. Here are sample questions from our survey:

(PIGEON)

Time: 2:00 PM, February 8, 2024
Location: Commercial Street
Historical Records:
• 3:00 PM, February 5, 2024: Hairdressing service at Commercial Street
• 12:30 PM, February 1, 2024: Nail service at Commercial Street
• 4:00 PM, January 28, 2024: Gym at Commercial Street
• 11:00 AM, January 25, 2024: Yoga studio at Commercial Street
• 2:30 PM, January 22, 2024: Eyelash service at Commercial Street
Prediction: Esteem needs, seeking services for appearance enhancement and fitness maintenance to boost confidence.

(LLMSREC-Syn)

[Same context and historical records as above]
Prediction: User likely seeking beauty or fitness services in the commercial street.

(DisenHCN)

[Same context and historical records as above]
Prediction: Beauty

We recruit 116 participants, including graduate students in user modeling and business professionals from life service platforms. To ensure comprehension, we provide examples and detailed explanations of each dimension at the beginning of the questionnaire. To mitigate fatigue and order effects, we randomly select 15 questions from a pool of 21 for each participant to evaluate. The average completion time is approximately 11 minutes.

A.5 Performance Optimization and Deployment Considerations

Our method is designed for near-line deployment scenarios where second-level latency is acceptable. As described in Section 3.3, predicted needs serve as one source in the recall stage, and recall computations can be performed asynchronously. The computational flow is triggered by changes in time and location, recalculating only when time shifts to the next half-hour interval or location changes significantly, resulting in infrequent updates. While online computations often face strict latency constraints in the tens of milliseconds, near-line applications can tolerate latencies of seconds or even longer (Li et al., 2021).

Despite moderate latency requirements, we have performed comprehensive optimizations to handle potential peak loads and ensure practical deployment feasibility.

A.5.1 LLM Inference Optimization

We employ several strategies to reduce LLM inference latency:

Model Fine-tuning: We use instruction tuning on smaller LLMs. As shown in Figure 5, a fine-tuned 3B parameter model achieves performance comparable to GPT-4o mini while significantly reducing computational requirements.

Inference Engine Optimization: We build upon existing frameworks like vLLM (Kwon et al., 2023) and SGLang (Zheng et al., 2024) that leverage techniques including KV Cache, PagedAttention, and RadixAttention for general LLM inference optimization.
Prompt 1:
You are a user on a life service platform. At [8:00 PM] in [Commercial Street], what kind of living need are you most likely to have?
1. Your following past behaviors are provided for reference:
   At 8:00 PM, ordered cocktail bar on Commercial Street due to the living need for social drinking.
   At 8:30 PM, ordered Japanese izakaya on Commercial Street due to the living need for good food.
   At 8:00 PM, ordered coffee shop at Office Area due to the living need for light refreshments.
   At 8:30 PM, ordered dessert café on Commercial Street due to the living need for light refreshments.
   At 7:30 PM, ordered Korean BBQ restaurant on Commercial Street due to the living need for group gathering.
2. Behaviors of other users in similar contexts are:
   At 8:00 PM, ordered pub on Commercial Street due to the living need for social drinking.
   At 7:00 PM, ordered café at Office Area due to the living need for light refreshments.
   At 8:00 PM, ordered hotpot restaurant on Commercial Street due to the living need for group gathering.
   At 8:30 PM, ordered sushi restaurant on Commercial Street due to the living need for good food.
   At 8:30 PM, ordered wine bar on Commercial Street due to the living need for social drinking.
   At 6:30 PM, ordered bubble tea at Office Area due to the living need for light refreshments.
   At 8:00 PM, ordered yakitori shop on Commercial Street due to the living need for good food.
   At 8:30 PM, ordered craft beer bar on Commercial Street due to the living need for social drinking.
   At 7:30 PM, ordered BBQ restaurant on Commercial Street due to the living need for group gathering.
   At 9:00 PM, ordered karaoke bar on Commercial Street due to the living need for recreation.
Considering the current time, location, and preferences indicated by previous consumption behaviors, please infer and describe your potential living needs.
Response 1:
At 8:00 PM on Commercial Street, 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, particularly at establishments that offer both good food and a social atmosphere. The pattern suggests I value places where I can spend quality time with others while enjoying dinner or drinks, rather than just grabbing a quick meal or refreshment.
Prompt 2:
You are a user on a life service platform. An inference about your current living need at [8:00 PM] in [Commercial Street], is [Response 1]. Please use the following human living needs framework to further refine this inference, making it align with the framework’s scope. Your response should be concise and as informative as possible, around 20 words.
Response 2:
Based on my physiological need for dining at restaurants and social belonging need for social gatherings, I seek venues that combine dining and socializing.

Figure 9: A working example of PIGEON.

Targeted Prefill Optimization: Since our average input length (approximately 400 tokens) is much larger than output length (approximately 20 tokens), the Prefill stage represents the main bottleneck. We restructure prompts to maximize common prefixes across queries without altering semantics. The optimized prompt structure is:

You are a user on a life service platform. Given time and location, what kind of living need are you most likely to have? Considering the current time, location, and preferences indicated by previous consumption behaviors, please infer and describe your potential living needs. Answer with around 20 words.
Time: [time] Location: [location]
Your following past behaviors are provided for reference: [Personal Relevant Records]
Behaviors of other users in similar contexts are: [Similar Users’ Relevant Records]

We implement acceleration by explicitly caching the common prefix (the first 60 tokens), similar to System Prompt optimizations in online chat services. We verify that this rewritten prompt maintains the model’s predictive performance while enabling significant acceleration.

We test performance on a fine-tuned Qwen2.5-3B model running on a single A100 GPU. Results are shown in Table 5, demonstrating significant latency reduction while maintaining accuracy. Our optimization achieves a 13.9x acceleration compared to Hugging Face Transformers inference.

Using SGLang with Prefix Caching at batch size 256, our method maintains P99 latency below 0.75 seconds while processing over 440 queries per second. There exists a trade-off between latency and QPS: larger batch sizes allow more queries to be processed in batches, increasing QPS but also increasing latency per query; smaller batch sizes reduce latency per query but decrease QPS. In actual deployment, batch size should be dynamically adjusted based on real-time load, potentially leveraging queuing theory. For latency-critical scenarios, setting batch size to 1 achieves an average latency
Table 5: Performance comparison of different inference optimization strategies on a single A100 GPU. P50/P99 refer to median and 99th percentile latency; QPS denotes queries per second.

<table>
  <tr>
    <th rowspan="2">Method</th>
    <th rowspan="2">Prefix Cache</th>
    <th colspan="3">Batch Size = 64</th>
    <th colspan="3">Batch Size = 128</th>
    <th colspan="3">Batch Size = 256</th>
  </tr>
  <tr>
    <th>P50 (s)</th>
    <th>P99 (s)</th>
    <th>QPS</th>
    <th>P50 (s)</th>
    <th>P99 (s)</th>
    <th>QPS</th>
    <th>P50 (s)</th>
    <th>P99 (s)</th>
    <th>QPS</th>
  </tr>
  <tr>
    <td>HF Transformers</td>
    <td>-</td>
    <td>2.224</td>
    <td>2.238</td>
    <td>29.26</td>
    <td>4.186</td>
    <td>4.215</td>
    <td>31.27</td>
    <td>8.166</td>
    <td>8.202</td>
    <td>32.08</td>
  </tr>
  <tr>
    <td>vLLM</td>
    <td>No</td>
    <td>0.298</td>
    <td>0.501</td>
    <td>210.80</td>
    <td>0.427</td>
    <td>0.441</td>
    <td>303.60</td>
    <td>0.706</td>
    <td>0.881</td>
    <td>354.57</td>
  </tr>
  <tr>
    <td>vLLM</td>
    <td>Yes</td>
    <td>0.282</td>
    <td>0.479</td>
    <td>221.33</td>
    <td>0.405</td>
    <td>0.428</td>
    <td>319.32</td>
    <td>0.670</td>
    <td>0.841</td>
    <td>375.42</td>
  </tr>
  <tr>
    <td>SGLang</td>
    <td>No</td>
    <td>0.260</td>
    <td>0.435</td>
    <td>232.96</td>
    <td>0.362</td>
    <td>0.374</td>
    <td>329.31</td>
    <td>0.629</td>
    <td>0.758</td>
    <td>415.43</td>
  </tr>
  <tr>
    <td>SGLang</td>
    <td>Yes</td>
    <td>0.250</td>
    <td>0.416</td>
    <td>249.18</td>
    <td>0.355</td>
    <td>0.356</td>
    <td>342.95</td>
    <td>0.579</td>
    <td>0.713</td>
    <td>446.31</td>
  </tr>
</table>

of 157ms.

Crucially, the optimizations we implement are strictly lossless, incurring no loss in accuracy. In practical applications, further acceleration can be achieved by incorporating techniques such as quantization and FP8 mixed-precision inference. Simple calculations indicate that using at most 8 A100 80G GPUs, our system can handle over 10 million queries per hour with no accuracy loss, demonstrating that online deployment is entirely feasible considering the inherently infrequent computation requirements of this method.

A.5.2 Other Component Optimizations

Behavioral Embedding Learning and Retrieval: User and context embeddings can be pre-computed offline, incurring no additional inference latency. Vector similarity search leverages mature open-source solutions like Milvus (Wang et al., 2021), which achieves throughputs of 15,000-20,000 QPS for nearest neighbor search on millions of vectors using cost-effective hardware while maintaining recall rates above 0.95. We plan to deploy using such established solutions, with potential targeted optimizations to further enhance performance.

Life Service Recall: The recall process, mapping flexible need descriptions to services, can also achieve low latency. Experiments on a consumer-grade GPU (Nvidia 4090) show that vectorizing 10,000 predicted need descriptions (output limited to approximately 20 words by prompt constraints) using the fine-tuned bge-base-en-v1.5 model takes an average of 8.92ms per description. Service vectors can be pre-computed, and matching need embeddings to the nearest service vectors can be efficiently handled by systems like Milvus, typically completing within 10ms.

These comprehensive optimizations demonstrate that online deployment is entirely feasible while maintaining the system’s prediction accuracy and meeting practical performance requirements for large-scale industrial applications.

A.6 Details of Comparison Methods

Here, we give a more detailed discussion of our utilized comparison methods.

Closed-set prediction methods:

• XGBoost (Chen and Guestrin, 2016): A widely used gradient-boosting model that builds an ensemble of decision trees to capture nonlinear interactions between user, time, and location features. Known for its robustness and strong generalization capabilities, XGBoost is effective in CTR tasks where feature relationships are complex and varied.

• DeepFM (Guo et al., 2017): This model integrates a deep neural network with a factorization machine (FM) to learn both low- and high-order feature interactions, such as the cross-features between user, time, and location. DeepFM effectively captures intricate dependencies without requiring extensive feature engineering, making it a strong baseline for closed-set need prediction.

• DCN (Wang et al., 2017): The Deep & Cross Network (DCN) combines a deep network and a cross network, which explicitly models cross-feature interactions in a multi-layered structure. By learning high-level feature combinations, DCN can model complex dependencies among user, time, and location, increasing the prediction accuracy within fixed need sets.

• XDeepFM (Lian et al., 2018): Extending DeepFM, XDeepFM includes a Compressed Interaction Network (CIN) to capture complex, higher-order feature interactions across layers. This component enhances the model’s ability to learn subtle cross-feature patterns between user, time, and location, making it a powerful model for structured need prediction tasks.

• EulerNet (Tian et al., 2023): A recent CTR model optimized for large-scale recommendation, EulerNet efficiently handles high-dimensional feature inputs. It incorporates a
multi-layered structure and leverages feature embeddings to support large-scale applications with diverse features, providing competitive performance in need prediction tasks.

• LightGCN (He et al., 2020): An effective and concise Graph Convolutional Network (GCN) model. In our implementation, LightGCN constructs graphs with user, spatiotemporal context, living needs, and life services as nodes. The graph includes four types of edges: user-living need edges, user-life service edges, spatiotemporal context-living need edges, and spatiotemporal context-life service edges. The training method for LightGCN follows the same approach as the encoder training described in the paper. By focusing on core graph embeddings and omitting complex transformation layers, LightGCN efficiently captures direct relationships, making it effective for need prediction based on user and context.

• HAN (Wang et al., 2019): The Heterogeneous Attention Network (HAN) constructs a multi-relational graph, incorporating diverse types of nodes and edges (e.g., user, time, location, need) into a unified structure. HAN uses attention mechanisms to weigh different types of relationships, allowing it to learn the relative importance of each edge in predicting closed-set needs within varied contexts. In our implementation, we construct meta-paths including user-need-user, user-service-user, and context-need-context to capture different semantic relationships, then apply node-level and semantic-level attention to learn comprehensive representations.

• DisenHCN (Li et al., 2022c): DisenHCN utilizes disentangled representations within a hypergraph framework, breaking down user preferences along dimensions like time and location. By isolating preference factors in separate subspaces, DisenHCN captures the unique aspects of user behavior in each dimension, providing a more refined prediction of user needs.

Open-set prediction methods:

• Zero-Shot CoT (Kojima et al., 2022): The Chain-of-Thought (CoT) zero-shot approach leverages the LLM’s generalization capability to predict needs based solely on current time and location, bypassing the need for historical data. This method serves as a baseline to evaluate how well the model can deduce user needs purely from situational context.

• ReLLa (Lin et al., 2024): ReLLa enhances LLM-based predictions by employing a text embedding model to convert user behavior history and spatiotemporal context into a vectorized format. During prediction, ReLLa retrieves the most relevant historical user records based on the current time and location, improving need prediction accuracy by leveraging similar past behaviors.

• LLMSRec-Syn (Wang and Lim, 2024): Extending ReLLa, LLMSRec-Syn retrieves similar historical behaviors not only from the target user but also from other users with similar patterns. This method leverages both individual and similar users’ behavior, enhancing the open-set need prediction process by incorporating both user-specific and community-based context.

A.7 A Working Example of PIGEON

Here, we provide a complete working example of PIGEON, as shown in Figure 9. It can be seen that first, a comprehensive and multi-faceted need description was generated. Then, PIGEON generates a more concentrated and structured need expression. This refined need expression can be used for downstream applications such as life service recall.

A.8 Rationale for Using Maslow’s Hierarchy

The choice of Maslow’s hierarchy as our theoretical foundation is motivated by several key considerations. First, Maslow’s model provides a comprehensive yet structured framework that naturally aligns with the hierarchical organization of life services on platforms. For instance, food delivery services correspond to physiological needs, while educational services map to higher-level growth needs. This natural alignment facilitates more systematic refinement of LLM predictions.

Second, Maslow’s theory has been extensively validated in consumer behavior research (Subramanyan and Tomas Gomez-Arias, 2008; Schewe, 1988; Cao et al., 2013), demonstrating strong correlations between its need levels and consumer purchase patterns. This empirical foundation makes it particularly relevant for our task of predicting needs that drive service consumption, as it helps ensure our predictions align with established patterns of human motivation and consumption behavior.

While simpler prompting approaches could potentially guide the LLM to avoid overly specific
predictions, Maslow’s framework offers additional benefits by providing a structured way to organize and validate need predictions against established psychological principles. Our ablation studies show modest but consistent improvements with this approach, suggesting it helps maintain prediction quality while adding theoretical grounding.

A.9 Cost Analysis
In our main experiment, we used GPT-4o-mini as the base large language model. The costs per 10,000 predictions are approximately:

• Zero-shot CoT: $0.09
• ReLLa: $0.21
• LLMSREC-Syn: $0.67
• PIGEON: $1.05

A.10 The List of Closed-Set Living Needs
For traditional closed-set living needs prediction, we extracted needs from user reviews and categorized them into 21 types under expert guidance. These living needs are:

<table>
  <tr>
    <th>1.</th>
    <td>Good food</td>
  </tr>
  <tr>
    <th>2.</th>
    <td>A quick meal</td>
  </tr>
  <tr>
    <th>3.</th>
    <td>Cooking at home</td>
  </tr>
  <tr>
    <th>4.</th>
    <td>A group meal</td>
  </tr>
  <tr>
    <th>5.</th>
    <td>Social drinking</td>
  </tr>
  <tr>
    <th>6.</th>
    <td>Business travel</td>
  </tr>
  <tr>
    <th>7.</th>
    <td>Personal grooming</td>
  </tr>
  <tr>
    <th>8.</th>
    <td>Group gathering</td>
  </tr>
  <tr>
    <th>9.</th>
    <td>Home supplies</td>
  </tr>
  <tr>
    <th>10.</th>
    <td>Physical fitness</td>
  </tr>
  <tr>
    <th>11.</th>
    <td>Pet companionship</td>
  </tr>
  <tr>
    <th>12.</th>
    <td>Light refreshments</td>
  </tr>
  <tr>
    <th>13.</th>
    <td>Family bonding</td>
  </tr>
  <tr>
    <th>14.</th>
    <td>Vehicle service</td>
  </tr>
  <tr>
    <th>15.</th>
    <td>Recreation</td>
  </tr>
  <tr>
    <th>16.</th>
    <td>Body relaxation</td>
  </tr>
  <tr>
    <th>17.</th>
    <td>Beauty</td>
  </tr>
  <tr>
    <th>18.</th>
    <td>Leisure travel</td>
  </tr>
  <tr>
    <th>19.</th>
    <td>Child education</td>
  </tr>
  <tr>
    <th>20.</th>
    <td>Skill development</td>
  </tr>
  <tr>
    <th>21.</th>
    <td>Daily essentials</td>
  </tr>
</table>

Reviewer paper summaries: This work aims to enhance living need prediction using large language models (LLMs). The authors argue that existing methods treat living need prediction as a closed-set problem, which introduces several limitations. To address this, the paper reformulates living need prediction as an open-set classification task and proposes PIGEON as a solution. Experiments on multiple datasets are conducted to evaluate the effectiveness of the proposed approach.

The paper introduces PIGEON, a system for open-set living need prediction that uses LLMs to handle the complexity of human needs for life service platforms. Unlike traditional closed-set classification methods, PIGEON allows for unrestricted need predictions by utilizing user behavior data, spatiotemporal context, and Maslow’s hierarchy of needs. The system retrieves relevant historical user records, refines predictions based on a structured framework, and adapts them for efficient life service recall

This article proposes an open set life demand prediction system, PIGEON, based on the Large Language Model (LLM), aimed at addressing the limitations of traditional closed set classification methods in capturing the diversity and complexity of user needs. By combining user behavior record retrieval, Maslow's hierarchy of requirements theory for requirement refinement, and a recall module based on fine-tuning text embedding models, PIGEON significantly outperforms existing methods in service recommendation tasks (with an average improvement of 19.37%). The experiment verified the rationality and specificity of generating requirement descriptions, and achieved efficient deployment of small LLMs through instruction fine-tuning.
Summary of strengths: 1. The research topic, that is, Open-Set Living Need Prediction, is interesting.
2. The incorporation of Maslow’s Hierarchy of Needs provides a well-motivated perspective.
3. Overall, this work is well-writing and easy-to-follow.

1. The use of open-set classification to model living needs provides flexibility in handling diverse, vague, or composite needs that traditional closed-set models cannot capture. This is a significant advancement in the field of personalized recommendation systems.
2. The system efficiently integrates behavior-aware embeddings from historical user data, significantly enhancing prediction accuracy by incorporating both individual and similar user records. The GNN-based retrieval method for user behavior data is particularly strong.

S1: For the first time, open set classification has been introduced into the field of life demand prediction, and a novel solution has been proposed by combining the generation ability of LLM with Maslow theory.

S2: Systematic design covering the entire process from user behavior modeling, requirement generation to service recall, with clear logic and mutual support between modules.

S3: Multi dimensional experiments were conducted on a real dataset (Meituan platform), including manual evaluation, to verify the effectiveness of the method.

S4: By fine-tuning instructions to reduce computational costs, supporting small-scale LLM deployment, and enhancing the feasibility of industrial implementation.
Summary of weaknesses: 1. The task definition is unclear. The paper lacks a clear and precise definition of the task. Several key terms, such as *need*, *need type*, and *consumed service*, are not adequately introduced. Furthermore, the relationships between these concepts are not explicitly discussed. The authors should clarify the task before delving into the proposed method.

2. The novelty should be enhanced. The proposed approach appears to largely follow existing methods, as it mainly employs a neural model to obtain text embeddings of users and context while leveraging LLMs to extract additional knowledge for enhanced need prediction. The authors should provide a more detailed discussion of how their method differs from prior work and highlight its unique contributions.

3. The motivation should be enhanced. Especially, the challenges of applying LLMs to need prediction remain somewhat unclear. 

   --Regarding the second challenge, why must LLMs align predictions with a structured representation of living needs? Moreover, what constitutes structured human living needs? These aspects require further clarification.

   --Regarding the third challenge, the relationships between need types and appropriate services are not well-defined. The paper should offer more details on these concepts, possibly with real-world examples, to help readers better understand the problem setting.

4. Some claims in this work seem questionable. The authors emphasize that their work achieves open-set living need prediction, which is positioned as the key contribution compared to existing approaches. While the implementation does produce open-set outputs from LLMs, the final results are obtained by retrieving matches from a predefined, closed-set ground-truth living needs.  This process closely resembles traditional closed-set living need prediction methods. As a result, it is unclear whether the proposed method truly accomplishes open-set living need prediction. The authors should provide further clarification or reconsider this claim.

Further optimization for high-latency environments is necessary for broader application. Test PIGEON on retail and education to establish its versatility and generalizability.

W1: The high latency problem of LLM inference has not been fully resolved, and no performance optimization solution has been provided in real-time scenarios.

W2: The experiment was only based on data from two Chinese cities and did not verify the applicability across regions or platforms.

W3: The integration of Maslow theory relies on implicit understanding of LLM, lacking quantitative analysis of the effectiveness of theoretical application (such as requirement hierarchy matching).

W4: Although privacy protection is mentioned, there is a lack of in-depth exploration of ethical issues such as bias and information cocoons that may arise from recommendation systems.
Comments, suggestions and typos: Please refer to Weakness

Further refine the mapping from open-set need predictions to available services, possibly by introducing a hybrid approach that combines LLM-based prediction with structured knowledge bases. Overall, PIGEON is a novel and well-executed approach to living need prediction, pushing the boundaries of personalized recommendation systems by tackling the open-set problem. However, scalability and service mapping remain areas for further refinement.

1. line 056: "making it making it impossible to list all possibilities" => "making it impossible to list all possibilities.”

2. line 299: "to to identify suitable life services" => "to identify suitable life services"

3. line 329: "closer to the query than those that cannot," => "closer to the query than services that cannot fulfill the need"

The gramma should be rechecked to ensure the paper's quailty.
Overall assessment scores: 3

3

2.5
Metareview score: 3
Metareview: This paper is a resubmission, with a new set of reviewers. The previous review scores were 2, 2, and 3.5, and a key change from the previous round is that the paper has been recategorized to NLP Applications instead of CSS+CA. This new track fits the paper better, though reception towards this paper is still somewhere in the middle (2.5, 3, 3). 

This paper focuses on the task of living need prediction (e.g. "relaxation and entertainment after work") to provide better personalized service recommendations. The proposed system, PIGEON, incorporates Maslow's hierarchy of needs and information around users' history to make these predictions, outcompeting baselines.
Summary of reasons to publish: Reviewers note that this work proposes a novel task and the paper includes extensive experiments. Two reviewers noted that the soundness of this work is acceptable.