ABSTRACT
Crowdsourcing allows to build online platforms that make use of the power of human intelligence to complete tasks that are difficult to tackle for current algorithms. Current approaches to crowdsourcing adopt a methodology where tasks are published on specialized web platforms to a group of networked workers who can pick their preferred tasks freely on a first-come-first-served basis. Although this approach has several advantages, however it doesn't consider workers differences and capabilities. With the vast number of tasks posted by the requesters every day it's a challenging issue to satisfy both workers and requesters. In this paper, a crowdsourcing recommendation approach is proposed and evaluated that is based on a push methodology. This method aims to help workers to instantly find best matching tasks according to their interests and qualifications as well help the requesters to pick from the crowd the best workers for their desired tasks.
- Howe, Jeff. The rise of crowdsourcing. Wired magazine 14.6 (2006): 1--4.Google Scholar
- Difallah, Djellel Eddine, et al. The dynamics of micro-task crowdsourcing: The case of amazon mturk. In Proceedings of the 24th international conference on world wide web. International World Wide Web Conferences Steering Committee, 2015.Google ScholarDigital Library
- Ipeirotis, Panagiotis G. Analyzing the amazon mechanical turk marketplace. XRDS: Crossroads, The ACM Magazine for Students, Forthcoming (2010).Google Scholar
- Chilton, Lydia B., et al. Task search in a human computation market. In Proceedings of the ACM SIGKDD workshop on human computation. ACM, 2010.Google ScholarDigital Library
- Wu, Meng-Lun, Chia-Hui Chang, and Rui-Zhe Liu. Integrating content-based filtering with collaborative filtering using co-clustering with augmented matrices. Expert systems with applications 41.6 (2014): 2754--2761.Google Scholar
- Chen, Gang, Fei Wang, and Changshui Zhang. Collaborative filtering using orthogonal nonnegative matrix trifactorization. Information Processing & Management 45.3 (2009): 368--379.Google ScholarDigital Library
- Zhou, Tom Chao, et al. Tagrec: Leveraging tagging wisdom for recommendation. 2009 International Conference on Computational Science and Engineering. Vol. 4. IEEE, 2009.Google ScholarDigital Library
- Tewari, Naveen C., et al. MapReduce implementation of variational Bayesian probabilistic matrix factorization algorithm. 2013 IEEE International Conference on Big Data. IEEE, 2013.Google ScholarCross Ref
- Lin, Chen, et al. Premise: Personalized news recommendation via implicit social experts. In Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, 2012.Google Scholar
- Paradarami, Tulasi K., Nathaniel D. Bastian, and Jennifer L. Wightman. A hybrid recommender system using artificial neural networks. Expert Systems with Applications 83 (2017): 300--313.Google ScholarDigital Library
- Wang, Yuanyuan, Stephen Chi-Fai Chan, and Grace Ngai. Applicability of demographic recommender system to tourist attractions: a case study on trip advisor. In Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 03. IEEE Computer Society, 2012.Google ScholarDigital Library
- Šerić, Ljiljana, Mila Jukić, and Maja Braović. Intelligent traffic recommender system. 2013 36th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEE, 2013.Google Scholar
- Pham, Manh Cuong, et al. A clustering approach for collaborative filtering recommendation using social network analysis. J. UCS 17.4 (2011): 583--604.Google Scholar
- Paradarami, Tulasi K., Nathaniel D. Bastian, and Jennifer L. Wightman. A hybrid recommender system using artificial neural networks. Expert Systems with Applications 83 (2017): 300--313.Google ScholarDigital Library
- Gunawardana, Asela, and Christopher Meek. A unified approach to building hybrid recommender systems. RecSys 9 (2009): 117--124.Google Scholar
- Schein, Andrew I., et al. Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2002.Google ScholarDigital Library
- Chen, Wei, et al. A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web 17.2 (2014): 271--284.Google ScholarDigital Library
- Feng, Zhi Ming, and Yi Dan Su. Application of Using Simulated Annealing to Combine Clustering with Collaborative Filtering for Item Recommendation. Applied Mechanics and Materials. Vol. 347. Trans Tech Publications, 2013.Google Scholar
- Meehan, Kevin, et al. Context-aware intelligent recommendation system for tourism. 2013 IEEE international conference on pervasive computing and communications workshops (PERCOM workshops). IEEE, 2013.Google ScholarCross Ref
- Yuen, Man-Ching, Irwin King, and Kwong-Sak Leung. Task matching in crowdsourcing. 2011 International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing. IEEE, 2011.Google ScholarDigital Library
- Difallah, Djellel Eddine, Gianluca Demartini, and Philippe Cudré-Mauroux. Pick-a-crowd: tell me what you like, and i'll tell you what to do. In Proceedings of the 22nd international conference on World Wide Web. ACM, 2013.Google ScholarDigital Library
- Lin, Christopher H., Ece Kamar, and Eric Horvitz. Signals in the silence: Models of implicit feedback in a recommendation system for crowdsourcing. Twenty-Eighth AAAI Conference on Artificial Intelligence. 2014.Google ScholarCross Ref
- Yuen, Man-Ching, Irwin King, and Kwong-Sak Leung. Task recommendation in crowdsourcing systems. In Proceedings of the first international workshop on crowdsourcing and data mining. ACM, 2012.Google ScholarDigital Library
- Ambati, Vamsi, Stephan Vogel, and Jaime Carbonell. Towards task recommendation in micro-task markets. Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence. 2011.Google Scholar
- Li, Qunwei, et al. Multi-object classification via crowdsourcing with a reject option. IEEE Transactions on Signal Processing 65.4 (2016): 1068--10.Google ScholarDigital Library
Index Terms
- Tasks Recommendation in Crowdsourcing based on Workers' Implicit Profiles and Performance History
Recommendations
Efficient Learning-Based Recommendation Algorithms for Top-N Tasks and Top-N Workers in Large-Scale Crowdsourcing Systems
The task and worker recommendation problems in crowdsourcing systems have brought up unique characteristics that are not present in traditional recommendation scenarios, i.e., the huge flow of tasks with short lifespans, the importance of workers’ ...
Task recommendation in crowdsourcing systems
CrowdKDD '12: Proceedings of the First International Workshop on Crowdsourcing and Data MiningIn crowdsourcing systems, tasks are distributed to networked people to complete such that a company's production cost can be greatly reduced. Obviously, it is not efficient that the amount of time for a worker spent on selecting a task is comparable ...
TaskRec: A Task Recommendation Framework in Crowdsourcing Systems
Crowdsourcing is evolving as a distributed problem-solving and business production model in recent years. In crowdsourcing paradigm, tasks are distributed to networked people to complete such that a company's production cost can be greatly reduced. In ...
Comments