  Sequential recommendation is one of fundamental tasks for Web applications.
Previous methods are mostly based on Markov chains with a strong Markov
assumption. Recently, recurrent neural networks (RNNs) are getting more and
more popular and has demonstrated its effectiveness in many tasks. The last
hidden state is usually applied as the sequence's representation to make
recommendation. Benefit from the natural characteristics of RNN, the hidden
state is a combination of long-term dependency and short-term interest to some
degrees. However, the monotonic temporal dependency of RNN impairs the user's
short-term interest. Consequently, the hidden state is not sufficient to
reflect the user's final interest. In this work, to deal with this problem, we
propose a Hierarchical Contextual Attention-based GRU (HCA-GRU) network. The
first level of HCA-GRU is conducted on the input. We construct a contextual
input by using several recent inputs based on the attention mechanism. This can
model the complicated correlations among recent items and strengthen the hidden
state. The second level is executed on the hidden state. We fuse the current
hidden state and a contextual hidden state built by the attention mechanism,
which leads to a more suitable user's overall interest. Experiments on two
real-world datasets show that HCA-GRU can effectively generate the personalized
ranking list and achieve significant improvement.
