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Scaling Law for Recommendation Models: Towards General-purpose User Representations. (arXiv:2111.11294v5 [cs.IR] UPDATED)
Nov. 23, 2022, 2:12 a.m. | Kyuyong Shin, Hanock Kwak, Su Young Kim, Max Nihlen Ramstrom, Jisu Jeong, Jung-Woo Ha, Kyung-Min Kim
cs.LG updates on arXiv.org arxiv.org
Recent advancement of large-scale pretrained models such as BERT, GPT-3,
CLIP, and Gopher, has shown astonishing achievements across various task
domains. Unlike vision recognition and language models, studies on
general-purpose user representation at scale still remain underexplored. Here
we explore the possibility of general-purpose user representation learning by
training a universal user encoder at large scales. We demonstrate that the
scaling law is present in user representation learning areas, where the
training error scales as a power-law with the amount …
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