Gig Services Recommendation Method for Fuzzy Requirement Description
Zhiying Tu, Xiaofei Xu, Qian Zhang, Hanming Zhang, Zhongjie Wang · 2017
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.