1 citations · 1 across the 4 of their papers we have counts for
4 papers
Infer As You Train: A Symmetric Paradigm of Masked Generative for Click-Through Rate Prediction
Moyu Zhang, Yujun Jin, Yun Chen +3
Generative models are increasingly being explored in click-through rate (CTR) prediction field to overcome the limitations of the conventional discriminative paradigm, which rely o…
DGenCTR: Towards a Universal Generative Paradigm for Click-Through Rate Prediction via Discrete Diffusion
Moyu Zhang, Yun Chen, Yujun Jin +2
Recent advances in generative models have inspired the field of recommender systems to explore generative approaches, but most existing research focuses on sequence generation, a p…
Global-Distribution Aware Scenario-Specific Variational Representation Learning Framework
Moyu Zhang, Yujun Jin, Jinxin Hu +1
With the emergence of e-commerce, the recommendations provided by commercial platforms must adapt to diverse scenarios to accommodate users' varying shopping preferences. Current m…
Distribution-Guided Auto-Encoder for User Multimodal Interest Cross Fusion
Moyu Zhang, Yongxiang Tang, Yujun Jin +2
Traditional recommendation methods rely on correlating the embedding vectors of item IDs to capture implicit collaborative filtering signals to model the user's interest in the tar…