Abstract
In crowdsourcing systems, task recommendation can help workers to find their right tasks faster as well as help requesters to receive good quality output quicker. However, previously proposed classification approach does not consider the dynamic scenarios of new workers and new tasks in the system. In this paper, we propose a Task Recommendation (TaskRec) framework based on a unified probabilistic matrix factorization, aiming to recommend tasks to workers in dynamic scenarios. Unlike traditional recommendation systems, workers do not provide their ratings on tasks in crowdsourcing systems, and thus we propose to transform worker behaviors into ratings. Complexity analysis shows that our framework is efficient and is scalable to large datasets. Finally, we conduct experiments on real-world datasets for performance evaluation.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ambati, V., Vogel, S., Carbonell, J.: Towards task recommendation in micro-task markets. In: AAAI 2011: Proceedings of the 25th AAAI Workshop in Human Computation. AAAI Publications (2011)
Cheng, C., Yang, H., King, I., Lyu, M.R.: Fused matrix factorization with geographical and social influence in location-based social networks. In: AAAI 2012: Proceedings of Twenty-Sixth Conference on Artificial Intelligence, Toronto, Canada (2012)
Chilton, L.B., Horton, J.J., Miller, R.C., Azenkot, S.: Task search in a human computation market. In: Proceedings of the ACM SIGKDD Workshop on Human Computation, HCOMP 2010, pp. 1–9. ACM, New York (2010)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Ma, H., King, I., Lyu, M.R.: Effective missing data prediction for collaborative filtering. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2007, pp. 39–46. ACM, New York (2007)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS 2007: Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems. Curran Associates, Inc. (2007)
Yuen, M.C., Chen, L.J., King, I.: A survey of human computation systems. In: CSE 2009: Proceedings of IEEE International Conference on Computational Science and Engineering, pp. 723–728. IEEE Computer Society (2009)
Yuen, M.C., King, I., Leung, K.S.: A survey of crowdsourcing systems. In: SocialCom 2011: Proceedings of the Third IEEE International Conference on Social Computing, pp. 766–773. IEEE Computer Society (2011)
Yuen, M.C., King, I., Leung, K.S.: Task matching in crowdsourcing. In: CPSCom 2011: Proceedings of the 4th IEEE International Conference on Cyber, Physical and Social Computing, pp. 409–412. IEEE Computer Society (2011)
Yuen, M.C., King, I., Leung, K.S.: Task recommendation in crowdsourcing systems. In: KDD 2012: Proceedings of ACM KDD 2012 Workshop on Data Mining and Knowledge Discovery with Crowdsourcing (CrowdKDD). ACM (2012)
Zhang, H., Horvitz, E., Miller, R.C., Parkes, D.C.: Crowdsourcing general computation. In: CHI 2011: Proceedings of ACM CHI 2011 Workshop on Crowdsourcing and Human Computation. ACM (2011)
Zhou, T.C., Ma, H., King, I., Lyu, M.R.: Tagrec: Leveraging tagging wisdom for recommendation. In: Proceedings of the 2009 International Conference on Computational Science and Engineering, vol. 04, pp. 194–199. IEEE Computer Society, Washington, DC (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yuen, MC., King, I., Leung, KS. (2012). TaskRec: Probabilistic Matrix Factorization in Task Recommendation in Crowdsourcing Systems. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_63
Download citation
DOI: https://doi.org/10.1007/978-3-642-34481-7_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34480-0
Online ISBN: 978-3-642-34481-7
eBook Packages: Computer ScienceComputer Science (R0)