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. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Ge, F., He, Y., Liu, J., Lv, X., Zhang, W., & Li, Y. (2011). A reinforcement learning based tag recommendation. In Advances in Intelligent and Soft Computing (Vol. 124, pp. 251–258). https://doi.org/10.1007/978-3-642-25658-5_31
Mendeley helps you to discover research relevant for your work.