Gig Services Recommendation Method for Fuzzy Requirement Description

In recent years, freelancer economy has been a new normalcy. In the supply-driven freelancer marketplace, people sell their capabilities or labor as service on the internet platform to help others with some particular micro-tasks. As this kind of human service ecosystem is at the fast growth stage, it is inundated with a variety of services whose quality is uneven. Quite often, when facing these services, customers hesitate to make the decision. The root causes of this hesitation are: ( 1) customers do not know these services well, even the explicit service category and description are provided; ( 2) customers do not know their own demands well. Most of the time, customers only have a general/fuzzy goal, but have no sense of the requirements in detail. Therefore, this study aims at proposing a human services recommendation method for fuzzy customer requirement. The experimental data of this study is collected from Fiverr.com, which is one prominent supply-driven human services marketplace. By analyzing the transaction data, any details of services, freelancers, customers, and their relations will be extracted to construct a supply-demand relation graph. In this study, customer's fuzzy requirement description will be transferred into a query subgraph, which is the input of an evolved subgraph matching algorithm. This algorithm will help to retrieve the recommendable services ( combinations). In addition, a guided Q&A approach is designed to complement customer's fuzzy requirement, so that subgraph matching algorithm can retrieve better results.