14 papers
DrEM: Dual-Side Robust Ensemble Ranking from Noisy User Preference Predictions in Video Recommendation
Canwei Huang, Tiantian He, Xiaoxiao Xu +5
Industrial video recommendation systems typically adopt a multi-stage architecture. At the ensemble ranking stage, multi-dimensional user preference predictions (pxtrs) from an ups…
Uncertainty as Remedy: Mitigating Satisfaction Label Bias in Short Video Multi-Objective Ensemble Ranking
Zonghe Shao, Tiantian He, Xiaoxiao Xu +6
The core objective of short video recommendation is to model users' unobservable true satisfaction with recommended videos. As the dominant industrial framework, end-to-end multi-o…
HGenPush: A Heterogeneous Generative Recommendation Architecture for Industrial Push Notification Systems
Xiao Liang, Jiali Feng, Xin Feng +10
With the explosive growth of content platforms, recommendation systems need to better satisfy user demands to enhance user satisfaction and retention. Taking short-video platforms…
From Bootstrapping to Sequence Modeling: A Unified Generative Framework for Personalized Landing-Page Modeling
Fan Li, Chang Meng, Jiaqi Fu +6
Modern online platforms increasingly adopt multi-page architectures to accommodate diverse user needs. On these platforms, page navigation (the process of directing users to specif…
SGR: Stepwise Semantic-Guided Reasoning in Latent Space for Generative Recommendation
Zihao Guo, Jian Wang, Ruxin Zhou +6
Generative Recommendation (GR) has emerged as a transformative paradigm with its end-to-end generation advantages. However, existing GR methods primarily focus on direct Semantic I…
UniRank: Unified List-wise Reranking via Confidence-Ordered Denoising
Pengyue Jia, Hailan Yang, Shuchang Liu +7
List-wise reranking arranges a request-specific pool of candidate items into an ordered slate that maximizes user satisfaction. Existing generative rerankers fall into two paradigm…