MIC: Model-agnostic Integrated Cross-channel Recommender

Author:

Nie Ping1,Lu Yujie2,Zhang Shengyu3,Zhao Ming1,Xie Ruobing1,Wang William Yang2,Ren Yi1

Affiliation:

1. Tencent, Beijing, China

2. University of California, Santa Barbara, Santa Barbara, CA, USA

3. Zhejiang University, Hangzhou, China

Publisher

ACM

Reference43 articles.

1. Mehdi Azabou , Mohammad Gheshlaghi Azar , Ran Liu, Chi-Heng Lin, Erik C. Johnson, Kiran Bhaskaran-Nair, Max Dabagia, Keith B. Hengen, William Gray-Roncal, Michal Valko, and Eva L. Dyer. 2021 . Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction. ArXiv abs/2102.10106 (2021). Mehdi Azabou, Mohammad Gheshlaghi Azar, Ran Liu, Chi-Heng Lin, Erik C. Johnson, Kiran Bhaskaran-Nair, Max Dabagia, Keith B. Hengen, William Gray-Roncal, Michal Valko, and Eva L. Dyer. 2021. Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction. ArXiv abs/2102.10106 (2021).

2. TasteWeights

3. Controllable Multi-Interest Framework for Recommendation

4. Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey E. Hinton . 2020. A Simple Framework for Contrastive Learning of Visual Representations . In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13--18 July 2020 , Virtual Event (Proceedings of Machine Learning Research , Vol. 119). PMLR, 1597-- 1607 . http://proceedings.mlr.press/v119/chen20j.html Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13--18 July 2020, Virtual Event (Proceedings of Machine Learning Research, Vol. 119). PMLR, 1597--1607. http://proceedings.mlr.press/v119/chen20j.html

5. Learning User and Product Distributed Representations Using a Sequence Model for Sentiment Analysis

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Contrastive Self-supervised Learning in Recommender Systems: A Survey;ACM Transactions on Information Systems;2023-11-08

2. Future Augmentation with Self-distillation in Recommendation;Lecture Notes in Computer Science;2023

3. Multi-granularity Item-Based Contrastive Recommendation;Database Systems for Advanced Applications;2023

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