5 papers · 1 filter
Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation
Guoqing Hu, An Zhang. Shuchang Liu, Wenyu Mao +7
Recommenders aim to rank items from a discrete item corpus in line with user interests, yet suffer from extremely sparse user preference data. Recent advances in diffusion models h…
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…
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…
Representation Quantization for Collaborative Filtering Augmentation
Yunze Luo, Yinjie Jiang, Gaode Chen +9
As the core algorithm in recommendation systems, collaborative filtering (CF) algorithms inevitably face the problem of data sparsity. Since CF captures similar users and items for…
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…