  E-commerce business is revolutionizing our shopping experiences by providing
convenient and straightforward services. One of the most fundamental problems
is how to balance the demand and supply in market segments to build an
efficient platform. While conventional machine learning models have achieved
great success on data-sufficient segments, it may fail in a large-portion of
segments in E-commerce platforms, where there are not sufficient records to
learn well-trained models. In this paper, we tackle this problem in the context
of market segment demand prediction. The goal is to facilitate the learning
process in the target segments by leveraging the learned knowledge from
data-sufficient source segments. Specifically, we propose a novel algorithm,
RMLDP, to incorporate a multi-pattern fusion network (MPFN) with a
meta-learning paradigm. The multi-pattern fusion network considers both local
and seasonal temporal patterns for segment demand prediction. In the
meta-learning paradigm, transferable knowledge is regarded as the model
parameter initialization of MPFN, which are learned from diverse source
segments. Furthermore, we capture the segment relations by combining
data-driven segment representation and segment knowledge graph representation
and tailor the segment-specific relations to customize transferable model
parameter initialization. Thus, even with limited data, the target segment can
quickly find the most relevant transferred knowledge and adapt to the optimal
parameters. We conduct extensive experiments on two large-scale industrial
datasets. The results justify that our RMLDP outperforms a set of
state-of-the-art baselines. Besides, RMLDP has been deployed in Taobao, a
real-world E-commerce platform. The online A/B testing results further
demonstrate the practicality of RMLDP.
