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cs.IR2026
RoTE: Coarse-to-Fine Multi-Level Rotary Time Embedding for Sequential Recommendation
Haolin Zhang, Longtao Xiao, Guohao Cai +2
Sequential recommendation models have been widely adopted for modeling user behavior. Existing approaches typically construct user interaction sequences by sorting items according…
cs.IR2025
FAIR: Focused Attention Is All You Need for Generative Recommendation
Longtao Xiao, Haolin Zhang, Guohao Cai +6
Recently, transformer-based generative recommendation has garnered significant attention for user behavior modeling. However, it often requires discretizing items into multi-code r…
cs.IR2024
CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation
Jieming Zhu, Mengqun Jin, Qijiong Liu +3
Embedding-based retrieval serves as a dominant approach to candidate item matching for industrial recommender systems. With the success of generative AI, generative retrieval has r…