1 citations · 2 across the 29 of their papers we have counts for
8 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…
Structural Entropy Guided Probabilistic Coding
Xiang Huang, Hao Peng, Li Sun +4
Probabilistic embeddings have several advantages over deterministic embeddings as they map each data point to a distribution, which better describes the uncertainty and complexity…
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…
A Unified Framework for Cross-Domain Recommendation
Jiangxia Cao, Shen Wang, Gaode Chen +4
In addressing the persistent challenges of data-sparsity and cold-start issues in domain-expert recommender systems, Cross-Domain Recommendation (CDR) emerges as a promising method…
Moment&Cross: Next-Generation Real-Time Cross-Domain CTR Prediction for Live-Streaming Recommendation at Kuaishou
Jiangxia Cao, Shen Wang, Yue Li +6
Kuaishou, is one of the largest short-video and live-streaming platform, compared with short-video recommendations, live-streaming recommendation is more complex because of: (1) te…
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…