Scaling Law of Large Sequential Recommendation Models

Scaling of neural networks has recently shown great potential to improve the model capacity in various fields. Specifically, model performance has a power-law relationship with model size or data size, which provides important guidance for the development of large-scale models. However, there is still limited understanding on the scaling effect of user behavior models in recommender systems, where the unique data characteristics (e.g., data scarcity and sparsity) pose new challenges in recommendation tasks.

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Publication Details

Published
2024-10-08
DOI
https://doi.org/10.1145/3640457.3688129
Citations
17
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
13.25

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article

Scaling Law of Large Sequential Recommendation Models

Ji-Rong Wen, Wayne Xin Zhao, Yupeng Hou, Hongyu Lu et al.
17 citations
Recommender Systems and Techniques
13.25
article

Scaling Law of Large Sequential Recommendation Models

Ji-Rong Wen, Wayne Xin Zhao, Yupeng Hou, Hongyu Lu, Gaowei Zhang, Yu Chen
article en
17 citations

Abstract

Scaling of neural networks has recently shown great potential to improve the model capacity in various fields. Specifically, model performance has a power-law relationship with model size or data size, which provides important guidance for the development of large-scale models. However, there is still limited understanding on the scaling effect of user behavior models in recommender systems, where the unique data characteristics (e.g., data scarcity and sparsity) pose new challenges in recommendation tasks.

Tencent (China) (CN), University of California San Diego (US), Renmin University of China (CN)
National Natural Science Foundation of China, Renmin University of China
Openalex Percentile: Top 1%
Recommender Systems and Techniques
13.25
DOIOpen Access PDFOpenAlex
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Scaling Law of Large Sequential Recommendation Models — Ji-Rong Wen, Wayne Xin Zhao, et al. · (2024) | TGRS Research Map | TGRS