Enhancing Multi-field B2B Cloud Solution Matching via Contrastive Pre-training

Cloud solutions have gained significant popularity in the technology While there have been some studies focusing on designing effective industry as they offer a combination of services and tools to matching systems [1, 18, 20, 23, 29, 32, 35], none of these tackle specific problems. However, despite their widespread use, the works have explored the matching of cloud solutions and their customers, task of identifying appropriate company customers for a specific which holds significant business value. In Huawei Cloud, target solution to the sales team of a solution provider remains a the scenario is manual-driven, wherein our model identifies a list complex business problem that existing matching systems have of the top matching companies to the sales team associated with yet to adequately address. In this work, we study the B2B solution a specific solution. The sales team then manually reviews this list matching problem and identify two main challenges of this scenario: and proceeds with promoting the solution to those companies. This (1) the modeling of complex multi-field features and (2) the limited, specific scenario can be considered a matching problem, with the incomplete, and sparse transaction data. To tackle these challenges, primary goal being the identification of appropriate companies we propose a framework CAMA, which is built with a hierarchical (customers) for the sales teams to target in their promotion efforts.