15 papers
PSG: Pair-Space Generation for Efficient Generative Reranking
Chao Feng, Li Ma, Xiancheng Gao +3
The paper introduces Pair-Space Generation (PSG), which generates ordered item pairs instead of single items to speed up generative reranking in recommender systems while preservin…
DIRECTOR: Dynamic Index-based Recommendation with Transport-Optimized Retrieval
Yuanhao Pu, Chenghao Zhang, Chao Feng +2
The paper introduces DIRECTOR, a parallel reranking framework that uses dynamic retrieval indices and entropy‑regularized optimal transport to generate duplicate‑free recommendatio…
Denoising Neural Reranker for Recommender Systems
Wenyu Mao, Shuchang Liu, Hailan Yang +9
For multi-stage recommenders in industry, a user request would first trigger a simple and efficient retriever module that selects and ranks a list of relevant items, then the recom…
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 +9
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…