A Task Recommendation Model for Mobile Crowdsourcing Systems based on Dwell-time

被引：3

作者：

Wang, Yingjie ^{[1
]}

Tong, Xiangrong ^{[1
]}

He, Zaobo ^{[2
]}

Gao, Yang ^{[1
]}

Wang, Kai ^{[1
]}

机构：

[1] Yantai Univ, Sch Comp & Control Engn, Yantai, Peoples R China

[2] Georgia State Univ, Dept Comp Sci, Atlanta, GA 30303 USA

来源：

PROCEEDINGS OF 2016 IEEE INTERNATIONAL CONFERENCES ON BIG DATA AND CLOUD COMPUTING (BDCLOUD 2016) SOCIAL COMPUTING AND NETWORKING (SOCIALCOM 2016) SUSTAINABLE COMPUTING AND COMMUNICATIONS (SUSTAINCOM 2016) (BDCLOUD-SOCIALCOM-SUSTAINCOM 2016) | 2016年

基金：

中国国家自然科学基金;

关键词：

component; formatting; style; styling; mobile crowdsourcing; recommendation model; dwell-time; reputation; participation frequencies; TRUST;

D O I：

10.1109/BDCloud-SocialCom-SustainCom.2016.36

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：170 / 177

页数：8

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