MTGR: Industrial-Scale Generative Recommendation Framework in Meituan

Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining . 1930–1939

Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018 · 1939

Earlier work this paper cites.

A coherent interpretation of AUC as a measure of aggregated classification performance. In Proceedings of the 28th International Conference on Machine Learning (ICML-11) . 657–664

Cesar Ferri, José Hernández-Orallo, and Peter A Flach. 2011 · 2011

Earlier work this paper cites.

xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining . 1754–1763

Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. 2018 · 2018

Earlier work this paper cites.

Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining . 1059–1068

Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018 · 2018

Earlier work this paper cites.

Scaling laws for neural language models

Original

Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2020

Earlier work this paper cites.

Search-based user interest modeling with lifelong sequential behavior data for click-through rate prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 2685–2692

Qi Pi, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, Xiaoqiang Zhu, and Kun Gai. 2020 · 2020

Earlier work this paper cites.

Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations. In Proceedings of the 14th ACM conference on recommender systems . 269–278

Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. 2020 · 2020

Earlier work this paper cites.

End-to-end user behavior retrieval in click-through rateprediction model

Original

Qiwei Chen, Changhua Pei, Shanshan Lv, Chao Li, Junfeng Ge, and Wenwu Ou. 2021 · 2021

Earlier work this paper cites.

Efficient sequence packing without cross-contamination: Accelerating large language models without impacting performance

Original

Mario Michael Krell, Matej Kosec, Sergio P Perez, and Andrew Fitzgibbon. 2021 · 2021

Earlier work this paper cites.

Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems. In Proceedings of the web conference 2021 . 1785–1797

Ruoxi Wang, Rakesh Shivanna, Derek Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed Chi. 2021 · 2021

Cited alongside, same era.

Flashattention: Fast and memory-efficient exact attention with io-awareness

Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. 2022 · 2022

Cited alongside, same era.

Torchrec: a pytorch domain library for recommendation systems. In Proceedings of the 16th ACM Conference on Recommender Systems . 482–483

Dmytro Ivchenko, Dennis Van Der Staay, Colin Taylor, Xing Liu, Will Feng, Rahul Kindi, Anirudh Sudarshan, and Shahin Sefati. 2022 · 2022

Cited alongside, same era.

Scaling vision transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 12104–12113

Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer. 2022 · 2022

Cited alongside, same era.

On the embedding collapse when scaling up recommendation models

Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management . 3777–3781

Ruidong Han, Qianzhong Li, He Jiang, Rui Li, Yurou Zhao, Xiang Li, and Wei Lin. 2024 · 2024

Later among the works it cites.

Twin v2: Scaling ultra-long user behavior sequence modeling for enhanced ctr prediction at kuaishou. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management . 4890–4897

Zihua Si, Lin Guan, ZhongXiang Sun, Xiaoxue Zang, Jing Lu, Yiqun Hui, Xingchao Cao, Zeyu Yang, Yichen Zheng, Dewei Leng, et al · 2024

Later among the works it cites.

HoME: Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou

Original

Xu Wang, Jiangxia Cao, Zhiyi Fu, Kun Gai, and Guorui Zhou. 2024 · 2024

Later among the works it cites.

Actions speak louder than words: Trillion-parameter sequential transducers for generative recommendations

Original

Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhaojie Gong, Fangda Gu, Michael He, et al · 2024