Budgeted Embedding Table For Recommender Systems-Reference-Cited by-同舟云学术

Budgeted Embedding Table For Recommender Systems

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

Qu Yunke¹^ORCID,Chen Tong¹^ORCID,Nguyen Quoc Viet Hung²^ORCID,Yin Hongzhi¹^ORCID

Affiliation:

1. The University of Queensland, Brisbane, Australia

2. Griffith University, Gold Coast, Australia

Funder

Australian Research Council

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3616855.3635778

Reference64 articles.

1. Anshumali Shrivastava Aditya Desai, Li Chou. 2022. Random Offset Block Embedding (ROBE) for compressed embedding tables in deep learning recommendation systems. In Proceedings of Machine Learning and Systems. 762--778.

2. Mathieu Blondel, Akinori Fujino, Naonori Ueda, and Masakazu Ishihata. 2016. Higher-Order Factorization Machines. In Proceedings of the 30th International Conference on Neural Information Processing Systems. 3359--3367.

3. Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han. 2020. Oncefor-all: Train one network and specialize it for efficient deployment. ICLR (2020).

4. Location-Aware Personalized News Recommendation With Deep Semantic Analysis

5. Ting Chen, Lala Li, and Yizhou Sun. 2020. Differentiable Product Quantization for End-to-End Embedding Compression. In Proceedings of the 37th International Conference on Machine Learning. 1617--1626.

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