TaskRec: Probabilistic matrix factorization in task recommendation in crowdsourcing systems
Refereed conference paper presented and published in conference proceedings

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摘要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. © 2012 Springer-Verlag.
著者Yuen M.-C., King I., Leung K.-S.
會議名稱19th International Conference on Neural Information Processing, ICONIP 2012
會議開始日12.11.2012
會議完結日15.11.2012
會議地點Doha
會議國家/地區卡塔爾
詳細描述organized by Springer,
出版年份2012
月份11
日期19
卷號7664 LNCS
期次PART 2
出版社Springer Verlag
出版地Germany
頁次516 - 525
國際標準書號9783642344800
國際標準期刊號0302-9743
語言英式英語
關鍵詞crowdsourcing, matrix factorization, task recommendation

上次更新時間 2020-25-10 於 00:50