Self-Attentive Sequential Recommendation

@article{Kang2018SelfAttentiveSR,
  title={Self-Attentive Sequential Recommendation},
  author={Wang-Cheng Kang and Julian McAuley},
  journal={2018 IEEE International Conference on Data Mining (ICDM)},
  year={2018},
  pages={197-206}
}
Sequential dynamics are a key feature of many modern recommender systems, which seek to capture the 'context' of users' activities on the basis of actions they have performed recently. [...] Key Method At each time step, SASRec seeks to identify which items are 'relevant' from a user's action history, and use them to predict the next item. Extensive empirical studies show that our method outperforms various state-of-the-art sequential models (including MC/CNN/RNN-based approaches) on both sparse and dense…Expand
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