Personal recommendation using deep recurrent neural networks in NetEase

@article{Wu2016PersonalRU,
  title={Personal recommendation using deep recurrent neural networks in NetEase},
  author={Sai Wu and Weichao Ren and Chengchao Yu and Gang Chen and Dongxiang Zhang and Jingbo Zhu},
  journal={2016 IEEE 32nd International Conference on Data Engineering (ICDE)},
  year={2016},
  pages={1218-1229}
}
Each user session in an e-commerce system can be modeled as a sequence of web pages, indicating how the user interacts with the system and makes his/her purchase. A typical recommendation approach, e.g., Collaborative Filtering, generates its results at the beginning of each session, listing the most likely purchased items. However, such approach fails to exploit current viewing history of the user and hence, is unable to provide a real-time customized recommendation service. In this paper, we… CONTINUE READING
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