Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation-Reference-Cited by-同舟云学术

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Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation

Published:2023-09-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 17th ACM Conference on Recommender Systems
language:
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Author:

Lai Vivian¹^ORCID,Chen Huiyuan²^ORCID,Yeh Chin-Chia Michael³^ORCID,Xu Minghua¹^ORCID,Cai Yiwei¹^ORCID,Yang Hao¹^ORCID

Affiliation:

1. Visa Research, USA

2. Research, Visa Research, USA

3. Visa Research, Visa Research, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3604915.3608831

Reference43 articles.

1. Maksym Andriushchenko and Nicolas Flammarion . 2022 . Towards understanding sharpness-aware minimization . In International Conference on Machine Learning. Maksym Andriushchenko and Nicolas Flammarion. 2022. Towards understanding sharpness-aware minimization. In International Conference on Machine Learning.

2. Denoising Self-Attentive Sequential Recommendation

3. Tops, Bottoms, and Shoes: Building Capsule Wardrobes via Cross-Attention Tensor Network

4. Graph Neural Transport Networks with Non-local Attentions for Recommender Systems

5. Adversarial Graph Perturbations for Recommendations at Scale

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