Task assignment for social-oriented crowdsourcing

Abstract

Crowdsourcing has become an efficient measure to solve machine-hard problems by embracing group wisdom, in which tasks are disseminated and assigned to a group of workers in the way of open competition. The social relationships formed during this process may in turn contribute to the completion of future tasks. In this sense, it is necessary to take social factors into consideration in the research of crowdsourcing. However, there is little work on the interactions between social relationships and crowdsourcing currently. In this paper, we propose to study such interactions in those social-oriented crowdsourcing systems from the perspective of task assignment. A prototype system is built to help users publish, assign, accept, and accomplish location-based crowdsourcing tasks as well as promoting the development and utilization of social relationships during the crowdsourcing. Especially, in order to exploit the potential relationships between crowdsourcing workers and tasks, we propose a “worker-task” accuracy estimation algorithm based on a graph model that joints the factorized matrixes of both the user social networks and the history “worker-task” matrix. With the worker-task accuracy estimation matrix, a group of optimal worker candidates is efficiently chosen for a task, and a greedy task assignment algorithm is proposed to further the matching of worker-task pairs among multiple crowdsourcing tasks so as to maximize the overall accuracy. Compared with the similarity based task assignment algorithm, experimental results show that the average recommendation success rate increased by 3.67%; the average task completion rate increased by 6.17%; the number of new friends added per week increased from 7.4 to 10.5; and the average task acceptance time decreased by 8.5 seconds.

Keywords

crowdsourcing / social networks / task assignment

References

[1] Howe J. The rise of crowdsourcing. Wired Magazine, 2006, 14(6): 1–4

[2] Law E, Ahn L v. Human computation. Synthesis Lectures on Artificial Intelligence and Machine Learning, 2011, 5(3): 1–121

[3] Brabham D C. Crowdsourcing as a model for problem solving: an introduction and cases. Convergence, 2008, 14(1): 75–90

[4] Stone P, Brooks R, Brynjolfsson E, Calo R, Etzioni O, Hager G, Hirschberg J, Kalyanakrishnan S, Kamar E, Kraus S, Leyton-Brown K, Parkes D, Press W, Saxenian A, Shah J, Tambe M, Teller A. Artificial intelligence and life in 2030. Technical Report, One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, 2016

[5] Boudreau K J, Lakhani K R. Using the crowd as an innovation partner. Harvard Business Review, 2013, 91(4): 60–68

[6] King I, Lyu M R, Ma H. Introduction to social recommendation. In: Proceedings of the 19th International Conference on World WideWeb. 2010, 1355–1356

[7] Tang J L, Hu X, Liu H. Social recommendation: a review. Social Network Analysis and Mining, 2013, 3(4): 1113–1133

[8] Bandiera O, Barankay I, Rasul I. Social incentives in the workplace. The Review of Economic Studies, 2010, 77(2): 417–458

[9] Salakhutdinov R, Mnih A. Probabilistic matrix factorization. In: Proceedings of the 20th International Conference on Neural Information Processing Systems. 2007, 1257–1264

[10] Ambati V, Vogel S, Carbonell J. Towards task recommendation in microtask markets. In: Proceedings of the 11th AAAI Conference on Human Computation. 2011, 80–83

[11] Yuen M C, King I, Leung K S. Taskrec: probabilistic matrix factorization in task recommendation in crowdsourcing systems. In: Proceedings of the 19th International Conference on Neural Information Processing. 2012, 516–525

[12] Fan J, Li G L, Ooi B C, Tan K l, Feng J H. icrowd: an adaptive crowdsourcing framework. In: Proceedings of the ACM International Conference on Management of Data. 2015, 1015–1030

[13] Ho C J, Vaughan J W. Online task assignment in crowdsourcing markets. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence. 2012, 45–51

[14] Zheng Y D, Wang J N, Li G L, Cheng R, Feng J H. QASCA: a qualityaware task assignment system for crowdsourcing applications. In: Proceedings of the ACM International Conference on Management of Data. 2015, 1031–1046

[15] Wang L, Yu Z W, Han Q, Guo B, Xiong H Y. Multi-objective optimization based allocation of heterogeneous spatial crowdsourcing tasks. IEEE Transactions on Mobile Computing, 2018, 17(7): 1637–1650

[16] Gummidi S R B, Xie X K, Pedersen T B. A survey of spatial crowdsourcing. ACM Transactions on Database Systems, 2019, 44(2): 1–46

[17] Tong Y X, Yuan Y, Cheng Y R, Chen L, Wang G R. Survey on spatiotemporal crowdsourced data management techniques. Journal of Software, 2017, 28(1): 35–58 (in Chinese)

[18] Tong Y X, She J Y, Ding B, Wang L B, Chen L. Online mobile micro-task allocation in spatial crowdsourcing. In: Proceedings of the 32nd IEEE International Conference on Data Engineering. 2016, 49–60

[19] Tong Y X, She J Y, Ding B L, Chen L, Wo T Y, Xu K. Online minimum matching in real-time spatial data: experiments and analysis. Proceedings of the VLDB Endowment, 2016, 9(12): 1053–1064

[20] Tong Y X, She J Y, Meng R. Bottleneck-aware arrangement over eventbased social networks: the max-min approach. World Wide Web, 2016, 19(6): 1151–1177

[21] Tong Y X, Cao C C, Chen L. TCS: efficient topic discovery over crowdoriented service data. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014, 861–870

[22] Song T S, Tong Y X, Wang L B, She J Y, Yao B, Chen L, Xu K. Trichromatic online matching in real-time spatial crowdsourcing. In: Proceedings of the 33rd IEEE International Conference on Data Engineering. 2017, 1009–1020

[23] Deng D X, Shahabi C, Demiryurek U. Maximizing the number of worker’s self-selected tasks in spatial crowdsourcing. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2013, 324–333

[24] Feng J H, Li G L, Feng J H. A survey on crowdsourcing. Chinese Journal of Computers, 2015, 38(9): 1713–1726 (in Chinese)

[25] Ma H, Yang H X, Lyu M R, King I. Sorec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management. 2008, 931–940

[26] Jamali M, Ester M. A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 4th ACM Conference on Recommender Systems. 2010, 135–142

[27] Wang W Y, He Z P, Shi P, Wu W W, Jiang Y C, An B, Hao Z F, Chen B. Strategic social team crowdsourcing: forming a team of truthful workers for crowdsourcing in social networks. IEEE Transactions on Mobile Computing, 2019, 18(6): 1419–1432

[28] Cao X, Cong G, Jensen C S, Ng J J, Ooi B C, Phan N T, Wu D. Swors: a system for the efficient retrieval of relevant spatial Web objects. Proceedings of the VLDB Endowment, 2012, 5(12): 1914–1917

[29] Barabasi A, Albert R. Emergence of scaling in random networks. Science, 1999, 286(5439): 509–512

RIGHTS & PERMISSIONS

2020 Higher Education Press