RNN理解(介绍，常见的RNNs，训练RNNs-BPTT，python实现):
1) https://blog.csdn.net/heyongluoyao8/article/details/48636251
2) http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
本文的工作就是在RNN的基础上，改变了几个和输入矩阵的定义。（融入的时间信息sequential ）
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Context-aware Sequential Recommendation | |||||||||||
| 文献作者 | Qiang Liu | ||||||||||
| 文献发表年限 | 2016 | ||||||||||
| 文献关键字 | |||||||||||
| RNN理解; Sequential information add Contextual information | |||||||||||
| 摘要描述 | |||||||||||
| Since sequential information plays an importan- t role in modeling user behaviors, various sequential rec- ommendation methods have been proposed. Methods based on Markov assumption are widely-used, but independently combine several most recent components. Recently, Recurrent Neural Networks (RNN) based methods have been successfully applied in several sequential modeling tasks. However, for real- world applications, these methods have difficulty in modeling the contextual information, which has been proved to be very important for behavior modeling. In this paper, we propose a novel model, named Context-Aware Recurrent Neural Net- works (CA-RNN). Instead of using the constant input matrix and transition matrix in conventional RNN models, CA-RNN employs adaptive context-specific input matrices and adap- tive context-specific transition matrices. The adaptive context- specific input matrices capture external situations where user behaviors happen, such as time, location, weather and so on. And the adaptive context-specific transition matrices capture how lengths of time intervals between adjacent behaviors in historical sequences affect the transition of global sequential features. Experimental results show that the proposed CA- RNN model yields significant improvements over state-of-the- art sequential recommendation methods and context-aware recommendation methods on two public datasets, i.e., the Taobao dataset and the Movielens-1M dataset. | |||||||||||
