  Online Food Ordering Service (OFOS) is a popular location-based service that
helps people to order what you want. Compared with traditional e-commerce
recommendation systems, users' interests may be diverse under different
spatiotemporal contexts, leading to various spatiotemporal data distribution,
which limits the fitting capacity of the model. However, numerous current works
simply mix all samples to train a set of model parameters, which makes it
difficult to capture the diversity in different spatiotemporal contexts.
Therefore, we address this challenge by proposing a Bottom-up Adaptive
Spatiotemporal Model(BASM) to adaptively fit the spatiotemporal data
distribution, which further improve the fitting capability of the model.
Specifically, a spatiotemporal-aware embedding layer performs weight adaptation
on field granularity in feature embedding, to achieve the purpose of
dynamically perceiving spatiotemporal contexts. Meanwhile, we propose a
spatiotemporal semantic transformation layer to explicitly convert the
concatenated input of the raw semantic to spatiotemporal semantic, which can
further enhance the semantic representation under different spatiotemporal
contexts. Furthermore, we introduce a novel spatiotemporal adaptive bias tower
to capture diverse spatiotemporal bias, reducing the difficulty to model
spatiotemporal distinction. To further verify the effectiveness of BASM, we
also novelly propose two new metrics, Time-period-wise AUC (TAUC) and City-wise
AUC (CAUC). Extensive offline evaluations on public and industrial datasets are
conducted to demonstrate the effectiveness of our proposed modle. The online
A/B experiment also further illustrates the practicability of the model online
service. This proposed method has now been implemented on the Ele.me, a major
online food ordering platform in China, serving more than 100 million online
users.
