Abstract
TV program recommendation can help user find interested programs and improve user experience. The heterogeneous information of programs is important for alleviating the problem of data sparsity. In addition, the existing TV program recommendation methods are lacking in dynamics. This paper proposes a neural TV program recommendation based on dynamic long-short term interest (NPR-DLSTI), which mainly includes two modules: program and user encoder. In the program encoder module, we use convolutional neural network and attention mechanism to learn the heterogeneous information of the program and realize program representation. In the user encoder module, we use gated recurrent unit and personalized attention to learn the dynamic change law of users’ interests. Experiments on real data sets show that our method can effectively improve the effectiveness and dynamics of TV program recommendation than other existing models.
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