Corpus ID: 119304486

Personalized Context-aware Re-ranking for E-commerce Recommender Systems

@article{Pei2019PersonalizedCR,
  title={Personalized Context-aware Re-ranking for E-commerce Recommender Systems},
  author={Changhua Pei and Yi Zhang and Yongfeng Zhang and Fei Sun and Xiao Lin and Hanxiao Sun and Jian Wu and Peng Jiang and Wenwu Ou and Dan Pei},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.06813}
}
  • Changhua Pei, Yi Zhang, +7 authors Dan Pei
  • Published in ArXiv 2019
  • Computer Science
  • Highlight Information
    Ranking is a core task in E-commerce recommender systems, which aims at providing an ordered list of items to users. [...] Key Method The proposed re-ranking model can be easily deployed as a follow-up modular after ranking by directly using the existing feature vectors of ranking. It directly optimizes the whole recommendation list by employing a transformer structure to efficiently encode the information of all items in the list.Expand Abstract

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