Gated recurrent units based neural network for time heterogeneous feedback recommendation

Nowadays, recommender systems face the problem of time heterogeneous feedback recommendation, in which items are recommended according to several kinds of user feedback with time stamps. Previously proposed recurrent neural network based recommendation method (RNNRec) cannot analyze feedback sequences on multiple time scales, and gradient vanishing may occur when the model is trained through back propagation through time (BPTT) algorithm. To address these issues, we propose a gated recurrent units (GRU) based neural network to predict which items users will access in the future. The GRU layer in the model can analyze feedback sequences on multiple time scales and can avoid gradient vanishing during training. The proposed approach is verified on three large-scale real-life datasets, and the comparison indicates that the proposed approach outperforms several state-of-the-art methods.