Abstract
This paper proposes a reinforcement learning based tag recommendation algorithm to deal with the data sparseness that affects the performance stability of collaborative filtering algorithms. Our algorithm integrates user tags into traditional collaborative filtering algorithms and attaching importance to user interest shift in the process of user interest learning process. Empirical Cases of comparing with traditional collaborative filtering algorithms indicate that our recommend algorithm exhibits better performance competition.
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Ge, F., He, Y., Liu, J., Lv, X., Zhang, W., Li, Y. (2011). A Reinforcement Learning Based Tag Recommendation. In: Wang, Y., Li, T. (eds) Practical Applications of Intelligent Systems. Advances in Intelligent and Soft Computing, vol 124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25658-5_31
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DOI: https://doi.org/10.1007/978-3-642-25658-5_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25657-8
Online ISBN: 978-3-642-25658-5
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