A Task Recommendation Model for Mobile Crowdsourcing Systems Based on Dwell-Time
Yingjie Wang, Xiangrong Tong, Zaobo He, Yang Gao, Kai Wang · 2016
With the developments of mobile services, mobile crowdsourcing systems are attracting more and more attention. How to recommend user-preferred and trustful tasks for users is an important issue to improve efficiency of mobile crowd-sourcing systems. This paper proposes a task recommendation model for mobile crowdsourcing systems based on dwell-time. Considering both user similarity and task similarity, the recommendation probabilities of tasks are derived. Based on dwell-time, the latent recommendation probability of tasks can be predicted. In addition, trust of tasks is obtained based on their reputations and participation frequencies. Finally, we perform comprehensive experiments towards the Amazon metadata and YOOCHOOSE data sets to verify the effectiveness of the proposed recommendation model.