Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendation 文章

arXiv:2604.20861v3 Announce Type: replace-cross Abstract: Semantic IDs (SIDs) provide the discrete item vocabulary used by generative recommendation, but their quality depends on what item evidence is preserved before quantization. In product recommendation, surface metadata often misses latent usage intent, visual evidence may be only weakly reflected in text, and downstream policy learning provides sparse feedback about whether a generated SID corresponds to a semantically useful item. We introduce \textbf{DeepInterestGR}, an intent-enriched SID framework for generative recommendation. Before SID quantization, \textbf{CMSA} enriches item representations through two complementary evidence paths: recommendation-oriented VLM captions and projected image embeddings. \textbf{DCIM} then uses an LLM to mine item-side intent descriptors -- latent usage motivations implied by product content rather than personalized user states.