  Cloud solutions have gained significant popularity in the technology industry
as they offer a combination of services and tools to tackle specific problems.
However, despite their widespread use, the task of identifying appropriate
company customers for a specific target solution to the sales team of a
solution provider remains a complex business problem that existing matching
systems have yet to adequately address. In this work, we study the B2B solution
matching problem and identify two main challenges of this scenario: (1) the
modeling of complex multi-field features and (2) the limited, incomplete, and
sparse transaction data. To tackle these challenges, we propose a framework
CAMA, which is built with a hierarchical multi-field matching structure as its
backbone and supplemented by three data augmentation strategies and a
contrastive pre-training objective to compensate for the imperfections in the
available data. Through extensive experiments on a real-world dataset, we
demonstrate that CAMA outperforms several strong baseline matching models
significantly. Furthermore, we have deployed our matching framework on a system
of Huawei Cloud. Our observations indicate an improvement of about 30% compared
to the previous online model in terms of Conversion Rate (CVR), which
demonstrates its great business value.
