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Learning drifting user interest incrementally from numerically labeled feedbacks
Zhang Pin, Pu Juhua, Liu Yongli, Xiong Zhang*
School of Computer Science and Technology, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

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Download: PDF (0KB)   HTML 1KB   Export: BibTeX or EndNote (RIS)      Supporting Info
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Abstract�� Most incremental approaches for learning drifting user interests assume that data instances in user feedbacks are binary labeled. A novel incremental learning approach was presented which learns drifting user interests from numerically labeled feedbacks instead of binary labeled ones. User interests were modeled as a set of probabilistic concepts. Numerical instance labels were considered as probabilities that the user likes those instances. Feedbacks were used to update user interest models incrementally based on an exponential, recency-weighted average algorithm. Experimental results on different learning tasks showed that the approach outperforms existing approaches in numerically labeled feedback environment.
Keywords�� learning algorithms   reinforcement learning   fuzzy sets     
Received 2008-09-18;
Fund:

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About author: �� Ʒ(1983-),��,���ɹŰ�ͷ��,˶ʿ��,zhangpin@cse.buaa.edu.cn.
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Zhang Pin, Pu Juhua, Liu Yongli, Xiong Zhang.Learning drifting user interest incrementally from numerically labeled feedbacks[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2009,V35(9): 1057-1061
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