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cs.IR2025
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.IR2024
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…