Alleviating Cold-start Problem in CTR Prediction with A Variational
Embedding Learning Framework

Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework

17 January 2022

Papers citing "Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework"

8 / 8 papers shown

Title
The Evolution of Embedding Table Optimization and Multi-Epoch Training in Pinterest Ads Conversion Andrew Qiu Shubham Barhate Hin Wai Lui Runze Su Rafael Rios Müller Kungang Li Ling Leng Han Sun Shayan Ehsani Zhifang Liu 36 0 0 08 May 2025
Towards Distribution Matching between Collaborative and Language Spaces for Generative Recommendation Yi-cui Zhang Yiwen Zhang Y. X. R. Wang Tong Chen Hongzhi Yin 28 0 0 10 Apr 2025
FilterLLM: Text-To-Distribution LLM for Billion-Scale Cold-Start Recommendation Ruochen Liu Hao-Heng Chen Yuanchen Bei Zhenhong Zhou Lijia Chen Qijie Shen Feiran Huang Fakhri Karray Shuaiqiang Wang VLM 44 0 0 24 Feb 2025
Exploiting Epistemic Uncertainty in Cold-Start Recommendation Systems Yang Xiang Li Fan Chenke Yin Menglin Kong Chengtao Ji OffRL 47 0 0 22 Feb 2025
Incorporating Group Prior into Variational Inference for Tail-User Behavior Modeling in CTR Prediction Han Xu Taoxing Pan Zhiqiang Liu Xiaoxiao Xu Lantao Hu 26 0 0 19 Oct 2024
Integrating Natural Language Prompting Tasks in Introductory Programming Courses Chris Kerslake Paul Denny David H Smith IV James Prather Juho Leinonen Andrew Luxton-Reilly Stephen MacNeil 34 1 0 04 Oct 2024
Enhancing User Interest based on Stream Clustering and Memory Networks in Large-Scale Recommender Systems Peng Liu Nian Wang Cong Xu Min Zhao Bin Wang Yi Ren 206 0 0 21 May 2024
Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracing Moyu Zhang Xinning Zhu Chun-Xiao Zhang Feng Pan Wenchen Qian Hui Zhao 21 0 0 03 Sep 2023