# NEON: Living Needs Prediction System in Meituan

arXiv: https://arxiv.org/abs/2307.16644 · HTML: https://mlsanity.com/paper/2307.16644/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2023.

## Built on by (most cited first, 3 of 3)

- [LocalSearchBench: Benchmarking Agentic Search in Real-World Local Life Services](https://mlsanity.com/paper/2512.07436/) (2025)
- [LocalGPT: Benchmarking and Advancing Large Language Models for Local Life Services in Meituan](https://mlsanity.com/paper/2506.02720/) (2025)
- [Open-Set Living Need Prediction with Large Language Models](https://mlsanity.com/paper/2506.02713/) (2025)

## Abstract (arXiv)

Living needs refer to the various needs in human's daily lives for survival and well-being, including food, housing, entertainment, etc. On life service platforms that connect users to service providers, such as Meituan, the problem of living needs prediction is fundamental as it helps understand users and boost various downstream applications such as personalized recommendation. However, the problem has not been well explored and is faced with two critical challenges. First, the needs are naturally connected to specific locations and times, suffering from complex impacts from the spatiotemporal context. Second, there is a significant gap between users' actual living needs and their historical records on the platform. To address these two challenges, we design a system of living NEeds predictiON named NEON, consisting of three phases: feature mining, feature fusion, and multi-task prediction. In the feature mining phase, we carefully extract individual-level user features for spatiotemporal modeling, and aggregated-level behavioral features for enriching data, which serve as the basis for addressing two challenges, respectively. Further, in the feature fusion phase, we propose a neural network that effectively fuses two parts of features into the user representation. Moreover, we design a multi-task prediction phase, where the auxiliary task of needs-meeting way prediction can enhance the modeling of spatiotemporal context. Extensive offline evaluations verify that our NEON system can effectively predict users' living needs. Furthermore, we deploy NEON into Meituan's algorithm engine and evaluate how it enhances the three downstream prediction applications, via large-scale online A/B testing.