1 citations · 1 across the 8 of their papers we have counts for
9 papers
SWIM: Step-Wise Integrated Measure for Session-supervised List Evaluation in Generative Re-ranking
Yuanhao Pu, Chenghao Zhang, Chao Feng +6
Modern industrial recommender systems have increasingly adopted the Generator-Evaluator (G-E) framework for the re-ranking stage. Within this paradigm, the generator produces candi…
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…
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…
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…
From Local Indices to Global Identifiers: Generative Reranking for Recommender Systems via Global Action Space
Pengyue Jia, Xiaobei Wang, Yingyi Zhang +14
In modern recommender systems, list-wise reranking serves as a critical phase within the multi-stage pipeline, finalizing the exposed item sequence and directly impacting user sati…
Action-Aware Generative Sequence Modeling for Short Video Recommendation
Wenhao Li, Zihan Lin, Zhengxiao Guo +7
With the rapid development of the Internet, users have increasingly higher expectations for the recommendation accuracy of online content consumption platforms. However, short vide…
SOLAR: SVD-Optimized Lifelong Attention for Recommendation
Chenghao Zhang, Chao Feng, Yuanhao Pu +9
Attention mechanism remains the defining operator in Transformers since it provides expressive global credit assignment, yet its quadratic cost in sequence length N makes long-cont…
Towards End-to-End Alignment of User Satisfaction via Questionnaire in Video Recommendation
Na Li, Jiaqi Yu, Minzhi Xie +8
Short-video recommender systems typically optimize ranking models using dense user behavioral signals, such as clicks and watch time. However, these signals are only indirect proxi…
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…