2 papers
cs.IR2026
Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation
Kai Cheng, Hao Wang, Wei Guo +4
Generative models offer a promising paradigm for the final stage reranking in multi-stage recommender systems, with the ability to capture inter-item dependencies within reranked l…
cs.IR2024
The 2nd Workshop on Recommendation with Generative Models
Wenjie Wang, Yang Zhang, Xinyu Lin +7
The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This works…