User Behavior Modeling with Deep Learning for Recommendation: Recent Advances
Author:
Affiliation:
1. Huawei Noah's Ark Lab, China
2. Huawei Noah's Ark Lab, Singapore
3. University of Science and Technology of China, China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3604915.3609496
Reference20 articles.
1. Compressed Interaction Graph based Framework for Multi-behavior Recommendation
2. Zhicheng He , Weiwen Liu , Wei Guo , Jiarui Qin , Yingxue Zhang , Yaochen Hu , and Ruiming Tang . 2023. A Survey on User Behavior Modeling in Recommender Systems. arXiv preprint arXiv:2302.11087 ( 2023 ). Zhicheng He, Weiwen Liu, Wei Guo, Jiarui Qin, Yingxue Zhang, Yaochen Hu, and Ruiming Tang. 2023. A Survey on User Behavior Modeling in Recommender Systems. arXiv preprint arXiv:2302.11087 (2023).
3. Balázs Hidasi Massimo Quadrana and Alexandros Karatzoglou et al. 2016. Parallel recurrent neural network architectures for feature-rich session-based recommendations. In RecSys. Balázs Hidasi Massimo Quadrana and Alexandros Karatzoglou et al. 2016. Parallel recurrent neural network architectures for feature-rich session-based recommendations. In RecSys.
4. Yifan Hu Yehuda Koren and Chris Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets. In ICDM. Yifan Hu Yehuda Koren and Chris Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets. In ICDM.
5. Memory Bank Augmented Long-tail Sequential Recommendation
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