GRU 序列建议网络 (A Hierarchical Contextual Attention-based GRU Network for Sequential Recommendation)

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.

翻译：序列建议是网络应用的基本任务之一。先前的方法大多基于Markov 链链, 且有强大的Markov 假设。最近, 经常神经网络( RNN) 越来越受欢迎, 并在许多任务中显示出其有效性。最后隐藏状态通常用作序列表达方式, 以提出建议。从 RNN 的自然特性中受益, 隐藏状态是长期依赖性和短期兴趣的组合, 但是, RNN 的单调时间依赖性会损害用户的短期利益。因此, 隐藏状态不足以反映用户的最终利益。因此, 隐藏状态不足以反映用户的最终利益。在这项工作中, 我们提议建立一个基于高度环境关注的 GRU (HCA- GRU) 网络。以输入方式进行第一级。我们使用基于关注机制的最近投入来构建背景输入。这可以模拟最近项目之间的复杂关联, 并加强隐藏状态。第二层是隐藏状态不足以反映用户最终兴趣的状态和背景隐藏状态。为了解决这一问题, 我们建议采用基于高端环境环境的GRU 将个人端端端端端端端点定位, 来产生更有意义的个人端端端端端端端端端端点, 。