7 papers
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…
GoalRank: Group-Relative Optimization for a Large Ranking Model
Kaike Zhang, Xiaobei Wang, Shuchang Liu +7
Mainstream ranking approaches typically follow a Generator-Evaluator two-stage paradigm, where a generator produces candidate lists and an evaluator selects the best one. Recent wo…
Who You Are Matters: Bridging Topics and Social Roles via LLM-Enhanced Logical Recommendation
Qing Yu, Xiaobei Wang, Shuchang Liu +14
Recommender systems filter contents/items valuable to users by inferring preferences from user features and historical behaviors. Mainstream approaches follow the learning-to-rank…
From Generation to Consumption: Personalized List Value Estimation for Re-ranking
Kaike Zhang, Xiaobei Wang, Xiaoyu Yang +5
Re-ranking is critical in recommender systems for optimizing the order of recommendation lists, thus improving user satisfaction and platform revenue. Most existing methods follow…