213 citations · 570 across the 49 of their papers we have counts for
5 papers · 1 filter
CRM: Retrieval Model with Controllable Condition
Chi Liu, Jiangxia Cao, Rui Huang +5
Retrieval models for recommendation need to account for outcomes such as watch time while searching large item corpora efficiently. We present the Controllable Retrieval Model (CRM…
QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou
Xinchen Luo, Jiangxia Cao, Tianyu Sun +17
In recent years, with the significant evolution of multi-modal large models, many recommender researchers realized the potential of multi-modal information for user interest modeli…
KuaiFormer: Transformer-Based Retrieval at Kuaishou
Chi Liu, Jiangxia Cao, Rui Huang +4
In large-scale content recommendation systems, retrieval serves as the initial stage in the pipeline, responsible for selecting thousands of candidate items from billions of option…
MARM: Unlocking the Future of Recommendation Systems through Memory Augmentation and Scalable Complexity
Xiao Lv, Jiangxia Cao, Shijie Guan +7
Scaling-law has guided the language model designing for past years, however, it is worth noting that the scaling laws of NLP cannot be directly applied to RecSys due to the followi…
HoME: Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou
Xu Wang, Jiangxia Cao, Zhiyi Fu +2
In this paper, we present the practical problems and the lessons learned at short-video services from Kuaishou. In industry, a widely-used multi-task framework is the Mixture-of-Ex…