3 papers
cs.IR2026
Breaking the Likelihood Trap: Consistent Generative Recommendation with Graph-structured Model
Qiya Yang, Xiaoxi Liang, Zeping Xiao +5
Reranking, as the final stage of recommender systems, plays a crucial role in determining the final exposure, directly influencing user experience. Recently, generative reranking h…
cs.IR2026
DualGR: Generative Retrieval with Long and Short-Term Interests Modeling
Zhongchao Yi, Kai Feng, Xiaojian Ma +5
In large-scale industrial recommendation systems, retrieval must produce high-quality candidates from massive corpora under strict latency. Recently, Generative Retrieval (GR) has…
cs.IR2025
Non-autoregressive Generative Models for Reranking Recommendation
Yuxin Ren, Qiya Yang, Yichun Wu +3
Contemporary recommendation systems are designed to meet users' needs by delivering tailored lists of items that align with their specific demands or interests. In a multi-stage re…