2P-AGRCFN: Two Phase Attention Gated Recurrent Context Filtering Network for Sequential Recommender Systems

Abstract

The recent trends in recommender systems have focused on modeling long-term tastes as well as short-term preferences. The various recurrent architectures have used for sequence modeling in recommender systems, since each state is a combination of current and previous layer output recurrently. Although the Recurrent Neural Networks (RNNs) have the ability for modeling both long-term and short-term dependency to some extent, the monotonic nature of temporal dependency of RNN reduces the effect of short-term interests of the user. Thus final interests of the users can’t be predicted from the hidden states. We propose a Two Phase- Attention Gated Recurrent Context Filtering Network (2P-AGRCF) for dealing with user’s long-term dependency as well as short-term preferences. The first phase of 2P-AGRCFN is performed in the input level by constructing a contextual input using certain number of recent input contexts for handling user’s short-term interests. This can handle the correlation among recent inputs and leads to strong hidden states. In the second phase, the contextual-hidden state is computed by fusing the attention mechanism and the hidden state at current time step, which leads to the effective modeling of overall interest of the user on recommendation. We experiment our model with YooChoose DataSet and it shows efficacy in generating personalized as well as ranked recommendations.