Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendation
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 projecte...
| Authors | Yangchen Zeng, Jinze Wang |
|---|---|
| Primary category | cs.IR |
| Published | 3 March 2026 |
| Last updated at source | 1 June 2026 |
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