Towards Mitigating Dimensional Collapse of Representations in Collaborative Filtering-Reference-Cited by-同舟云学术

Towards Mitigating Dimensional Collapse of Representations in Collaborative Filtering

Published:2024-03-04 Issue: Volume: Page:106-115
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Container-title:Proceedings of the 17th ACM International Conference on Web Search and Data Mining
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Author:

Chen Huiyuan¹^ORCID,Lai Vivian¹^ORCID,Jin Hongye²^ORCID,Jiang Zhimeng²^ORCID,Das Mahashweta¹^ORCID,Hu Xia³^ORCID

Affiliation:

1. Visa Research, Palo Alto, USA

2. Texas A&M University, College Station, USA

3. Rice University, Houston, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3616855.3635832

Reference54 articles.

1. YM Asano, C Rupprecht, and A Vedaldi. 2019. Self-labelling via simultaneous clustering and representation learning. In International Conference on Learning Representations.

2. Efficient Maximal Coding Rate Reduction by Variational Forms

3. Mathilde Caron Ishan Misra Julien Mairal Priya Goyal Piotr Bojanowski and Armand Joulin. 2020. Unsupervised learning of visual features by contrasting cluster assignments. In Advances in Neural Information Processing Systems.

4. Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, and Yi Ma. 2022. ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction. Journal of Machine Learning Research (2022), 1--103.

5. Denoising Self-Attentive Sequential Recommendation

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