An Online-Updating Approach on Task Recommendation in Crowdsourcing Systems
Refereed conference paper presented and published in conference proceedings

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其它資訊
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. A number of previous works adopted active learning for task recommendation in crowdsourcing systems to achieve certain accuracy with a very low cost. However, the model updating methods in previous works are not suitable for real-world applications. In our paper, we propose a generic online-updating method for learning a factor analysis model, ActivePMF on TaskRec (Probabilistic Matrix Factorization with Active Learning on Task Recommendation Framework), for crowdsourcing systems. The larger the profile of a worker (or task) is, the less important is retraining its profile on each new work done. In case of the worker (or task) having large profile, our algorithm only retrains the whole feature vector of the worker (or task) and keeps all other entries in the matrix fixed. Besides, our algorithm runs batch update to further improve the performance. Experiment results show that our online-updating approach is accurate in approximating to a full retrain while the average runtime of model update for each work done is reduced by more than 90 % (from a few minutes to several seconds).
著者Man-Ching Yuen, Irwin King, Kwong-Sak Leung
會議名稱International Conference on Neural Information Processing
會議開始日16.10.2016
會議完結日21.10.2016
會議地點Kyoto
會議國家/地區日本
會議論文集題名Lecture Notes in Computer Science
出版年份2016
月份9
卷號9947
頁次91 - 101
國際標準書號978-3-319-46686-6
電子國際標準書號978-3-319-46687-3
國際標準期刊號0302-9743
語言英式英語
關鍵詞Crowdsourcing, Task recommendation

上次更新時間 2020-20-10 於 03:43