Attention with Long-Term Interval-Based Gated Recurrent Units for Modeling Sequential User Behaviors

Recommendations based on sequential User behaviors have become more and more common. Traditional methods depend on the premise of Markov processes and consider user behavior sequences as interests. However, they usually ignore the mining and representation of implicit features. Recently, recurrent neural networks (RNNs) have been adopted to leverage their power in modeling sequences and consider the dynamics of user behaviors. In order to better locate user preference, we design a network featuring Attention with Long-term Interval-based Gated Recurrent Units (ALI-GRU) to model temporal sequences of user actions. In the network, we propose a time interval-based GRU architecture to better capture long-term preferences and short-term intents when encoding user actions rather than the original GRU. And a specially matrix-form attention function is designed to learn weights of both long-term preferences and short-term user intents automatically. Experimental results on two well-known public datasets show that the proposed ALI-GRU achieves significant improvement than state-of-the-art RNN-based methods.