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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…
FlashEvaluator: Expanding Search Space with Parallel Sequence-Level Evaluation
Chao Feng, Yuanhao Pu, Chenghao Zhang +5
The Generator-Evaluator (G-E) framework generates K candidate sequences and uses an evaluator to select the highest-scoring one, which is widely used in recommender systems (RecSys…
Stratified Expert Cloning for Retention-Aware Recommendation at Scale
Chengzhi Lin, Annan Xie, Shuchang Liu +3
User retention is critical in large-scale recommender systems, significantly influencing online platforms' long-term success. Existing methods typically focus on short-term engagem…
Dreaming User Multimodal Representation Guided by The Platonic Representation Hypothesis for Micro-Video Recommendation
Chengzhi Lin, Hezheng Lin, Shuchang Liu +5
The proliferation of online micro-video platforms has underscored the necessity for advanced recommender systems to mitigate information overload and deliver tailored content. Desp…