Generative recommendation retrieves items by autoregressively generating semantic identifiers, but beam search may discard a target before its complete identifier is generated. Our preliminary analysis across three benchmarks shows that most missed targets are pruned within the first two decoding steps, highlighting th...
Hong-Liang Sun, Lian-Jie Li, Bolin Zhang et al.· 0 citations
Large language models (LLMs) offer new opportunities for recommendation by interpreting item descriptions, user instructions, and external knowledge through natural-language prompts. However, existing graph-augmented LLM recommenders often use knowledge graphs mainly as prompt-level evidence, leaving ranking decisions...
Yong Wang, Hongliang Sun, Jin-Lan Liu et al.· 0 citations
Temporal Autoregressive Alignment (TAAL) is proposed, which improves NDCG@10 over the standard baseline by 39.5%, and aligns the early-prefix distribution with a forward KL objective during training and during inference, it calibrates candidate scores with pointwise mutual information (PMI) to reduce the influence of g...
Lian-Jie Li, Zhi-Ying Tu, Dianhui Chu et al.· 0 citations
Experiments show that DuPLeR achieves robust performance in data-scarce KGC scenarios, and a dual-pathway multimodal enhancement module regulates message passing with query-relevant multimodal signals and supplements entity representations after graph propagation.
