When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation

@article{Tian2022WhenMM,
  title={When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation},
  author={Yu Tian and Jianxin Chang and Yannan Niu and Yang Song and Chenliang Li},
  journal={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2022}
}
  • Yu Tian, Jianxin Chang, Chenliang Li
  • Published 3 May 2022
  • Computer Science
  • Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Sequential recommendation aims at identifying the next item that is preferred by a user based on their behavioral history. Compared to conventional sequential models that leverage attention mechanisms and RNNs, recent efforts mainly follow two directions for improvement: multi-interest learning and graph convolutional aggregation. Specifically, multi-interest methods such as ComiRec and MIMN, focus on extracting different interests for a user by performing historical item clustering, while… 

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