TY - GEN
T1 - Gig Services Recommendation Method for Fuzzy Requirement Description
AU - Tu, Zhiying
AU - Xu, Xiaofei
AU - Zhang, Qian
AU - Zhang, Hanming
AU - Wang, Zhongjie
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/7
Y1 - 2017/9/7
N2 - 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.
AB - 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.
KW - Human services
KW - fuzzy requirement
KW - knowledge graph
KW - service recommendation
KW - subgraph matching
UR - https://www.scopus.com/pages/publications/85032361404
U2 - 10.1109/ICWS.2017.73
DO - 10.1109/ICWS.2017.73
M3 - 会议稿件
AN - SCOPUS:85032361404
T3 - Proceedings - 2017 IEEE 24th International Conference on Web Services, ICWS 2017
SP - 620
EP - 627
BT - Proceedings - 2017 IEEE 24th International Conference on Web Services, ICWS 2017
A2 - Chen, Shiping
A2 - Altintas, Ilkay
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th IEEE International Conference on Web Services, ICWS 2017
Y2 - 25 June 2017 through 30 June 2017
ER -