Abstract
Incremental approaches learn drifting user interests mainly from user feedbacks. Most of those existing approaches assume that data instances in user feedbacks are binary labeled. This paper presents a novel probabilistic approach that learns drifting user interests from numerically labeled feedbacks instead of binary labeled ones. The approach models user interests as a set of probabilistic concepts, considers numerical instance labels as probabilities that the user likes those instances, and uses feedbacks to update user interest models incrementally based on an exponential, recency-weighted average algorithm. Experimental results on different learning tasks show that the approach outperforms existing approaches in numerically labeled feedback environment.
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Zhang, P., Pu, J., Liu, Y., Xiong, Z. (2009). A Probabilistic Approach for Mining Drifting User Interest. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, QM. (eds) Advances in Data and Web Management. APWeb WAIM 2009 2009. Lecture Notes in Computer Science, vol 5446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00672-2_34
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DOI: https://doi.org/10.1007/978-3-642-00672-2_34
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