  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.
