Summary of Wukong: Towards a Scaling Law For Large-scale Recommendation, by Buyun Zhang et al.
Wukong: Towards a Scaling Law for Large-Scale Recommendation
by Buyun Zhang, Liang Luo, Yuxin Chen, Jade Nie, Xi Liu, Daifeng Guo, Yanli Zhao, Shen Li, Yuchen Hao, Yantao Yao, Guna Lakshminarayanan, Ellie Dingqiao Wen, Jongsoo Park, Maxim Naumov, Wenlin Chen
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces a novel network architecture and upscaling strategy, dubbed Wukong, to establish a scaling law in recommendation models. Unlike previous recommendation models, Wukong’s design enables the capture of diverse, any-order interactions through taller and wider layers. The model is evaluated on six public datasets, outperforming state-of-the-art models quality-wise. Additionally, Wukong demonstrates scalability on an internal large-scale dataset, retaining its superiority in quality over state-of-the-art models while extending beyond 100 GFLOP/example. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wukong is a new way to build recommendation models that makes them better and more efficient. It’s like a special recipe for building these models, which allows them to learn from complex data and make good recommendations. The researchers tested Wukong on several real-world datasets and found that it outperformed other state-of-the-art models. They also showed that Wukong can be scaled up to handle really large datasets without losing its ability to make good predictions. |
