REGER: Modeling inter-item Relations with Semantic IDs for Generative Recommendation
Personalized recommendation depends on learning semantic and collaborative relations among items from online content and user interactions. Generative recommendation (GR) offers a promising paradigm by autoregressively generating discrete semantic IDs (SIDs) of items. However, randomly initialized SID token embeddings...
Yu-Xi Huang, Si-Lin Zhou, Chenhao Wang et al.
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