STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation

@article{Liu2018STAMPSA,
  title={STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation},
  author={Qiao Liu and Yifu Zeng and Refuoe Mokhosi and Haibin Zhang},
  journal={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  year={2018}
}
  • Qiao LiuYifu Zeng Haibin Zhang
  • Published 19 July 2018
  • Computer Science
  • Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Predicting users' actions based on anonymous sessions is a challenging problem in web-based behavioral modeling research, mainly due to the uncertainty of user behavior and the limited information. Recent advances in recurrent neural networks have led to promising approaches to solving this problem, with long short-term memory model proving effective in capturing users' general interests from previous clicks. However, none of the existing approaches explicitly take the effects of users' current… 

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