1 citations · 1 across the 6 of their papers we have counts for
4 papers · 1 filter
SmartGR: Hierarchy and Beam-Aware Knowledge Distillation for Generative Recommendation
Ziheng Zhang, Yu Cui, Bohao Wang +6
Generative recommendation (GR) has emerged as a promising paradigm for recommender systems. Scaling up GR models can improve recommendation performance, but it also substantially i…
Trie-Aware Transformers for Generative Recommendation
Zhenxiang Xu, Jiawei Chen, Sirui Chen +5
Generative recommendation (GR) aligns with advances in generative AI by casting next-item prediction as token-level generation rather than score-based ranking. Most GR methods adop…
A Learnable Fully Interacted Two-Tower Model for Pre-Ranking System
Chao Xiong, Xianwen Yu, Wei Xu +3
Pre-ranking plays a crucial role in large-scale recommender systems by significantly improving the efficiency and scalability within the constraints of providing high-quality candi…
DOGR: Leveraging Document-Oriented Contrastive Learning in Generative Retrieval
Penghao Lu, Xin Dong, Yuansheng Zhou +3
Generative retrieval constitutes an innovative approach in information retrieval, leveraging generative language models (LM) to generate a ranked list of document identifiers (doci…