2 papers
cs.IR2026
DeGRe: Dense-supervised Generative Reranking for Recommendation
Chaotian Song, Jingyao Zhang, Chenghao Chen +6
In multi-stage recommender systems, reranking optimizes overall utility by capturing intra-list contextual dependencies, yet its central challenge lies in exploring optimal sequenc…
cs.IR2025
Pre-train and Fine-tune: Recommenders as Large Models
Zhenhao Jiang, Chenghao Chen, Hao Feng +5
In reality, users have different interests in different periods, regions, scenes, etc. Such changes in interest are so drastic that they are difficult to be captured by recommender…