An Efficient Large Recommendation Model: Towards a Resource-Optimal Scaling Law

no code implementations • 14 Feb 2025 • Songpei Xu, Shijia Wang, Da Guo, Xianwen Guo, Qiang Xiao, Fangjian Li, Chuanjiang Luo

The pursuit of scaling up recommendation models confronts intrinsic tensions between expanding model capacity and preserving computational tractability.

Feature Compression Recommendation Systems

LiRank: Industrial Large Scale Ranking Models at LinkedIn

no code implementations • 10 Feb 2024 • Fedor Borisyuk, Mingzhou Zhou, Qingquan Song, Siyu Zhu, Birjodh Tiwana, Ganesh Parameswaran, Siddharth Dangi, Lars Hertel, Qiang Xiao, Xiaochen Hou, Yunbo Ouyang, Aman Gupta, Sheallika Singh, Dan Liu, Hailing Cheng, Lei Le, Jonathan Hung, Sathiya Keerthi, Ruoyan Wang, Fengyu Zhang, Mohit Kothari, Chen Zhu, Daqi Sun, Yun Dai, Xun Luan, Sirou Zhu, Zhiwei Wang, Neil Daftary, Qianqi Shen, Chengming Jiang, Haichao Wei, Maneesh Varshney, Amol Ghoting, Souvik Ghosh

We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods.

Click-Through Rate Prediction Quantization

Bootstrapping Contrastive Learning Enhanced Music Cold-Start Matching

no code implementations • 5 Aug 2023 • Xinping Zhao, Ying Zhang, Qiang Xiao, Yuming Ren, Yingchun Yang

In short, given a cold-start song request, we expect to retrieve songs with similar audiences and then fastly push the cold-start song to the audiences of the retrieved songs to warm up it.

Contrastive Learning Representation Learning