DeepInterestGR: Mining Deep Multi-Interest Using Multi-Modal LLMs for Generative Recommendation
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arXiv
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| Format: | Preprint |
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2026
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| author | Zeng, Yangchen Yu, Zhenyu Hu, Zhiyuan Zhang, Wenxin Wang, Jinze Guo, Rongfeng |
| author_facet | Zeng, Yangchen Yu, Zhenyu Hu, Zhiyuan Zhang, Wenxin Wang, Jinze Guo, Rongfeng |
| contents | We introduce DeepInterestGR, a novel framework that integrates deep interest mining into the generative recommendation pipeline. This addresses the "Shallow Interest" problem - existing generative methods rely on surface-level textual features and fail to capture latent user motivations, limiting personalization depth and recommendation interpretability. Our approach leverages Multi-LLM Interest Mining (MLIM) via structured reasoning prompting, Reward-Labeled Deep Interest (RLDI) for quality control, and Interest-Enhanced Item Discretization (IEID) via RQ-VAE, combined with a two-stage SFT-GRPO training pipeline guided by an Interest-Aware Reward. We validate DeepInterestGR on three Amazon Review benchmarks (Beauty, Sports, Instruments), comparing against 14 state-of-the-art baselines including SASRec, BERT4Rec, TIGER, LC-Rec, and S-DPO. Our method achieves 5.8%-8.3% relative improvements on HR@10 and 7.7%-9.9% on NDCG@10 over the strongest baseline, with cross-domain generalization gains of +24.8%. These results provide evidence that incorporating deep semantic interests can effectively improve SID-based generative recommendation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_18907 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DeepInterestGR: Mining Deep Multi-Interest Using Multi-Modal LLMs for Generative Recommendation Zeng, Yangchen Yu, Zhenyu Hu, Zhiyuan Zhang, Wenxin Wang, Jinze Guo, Rongfeng Machine Learning Computer Vision and Pattern Recognition Computers and Society 68T50 H.3.3; I.2.7 We introduce DeepInterestGR, a novel framework that integrates deep interest mining into the generative recommendation pipeline. This addresses the "Shallow Interest" problem - existing generative methods rely on surface-level textual features and fail to capture latent user motivations, limiting personalization depth and recommendation interpretability. Our approach leverages Multi-LLM Interest Mining (MLIM) via structured reasoning prompting, Reward-Labeled Deep Interest (RLDI) for quality control, and Interest-Enhanced Item Discretization (IEID) via RQ-VAE, combined with a two-stage SFT-GRPO training pipeline guided by an Interest-Aware Reward. We validate DeepInterestGR on three Amazon Review benchmarks (Beauty, Sports, Instruments), comparing against 14 state-of-the-art baselines including SASRec, BERT4Rec, TIGER, LC-Rec, and S-DPO. Our method achieves 5.8%-8.3% relative improvements on HR@10 and 7.7%-9.9% on NDCG@10 over the strongest baseline, with cross-domain generalization gains of +24.8%. These results provide evidence that incorporating deep semantic interests can effectively improve SID-based generative recommendation. |
| title | DeepInterestGR: Mining Deep Multi-Interest Using Multi-Modal LLMs for Generative Recommendation |
| topic | Machine Learning Computer Vision and Pattern Recognition Computers and Society 68T50 H.3.3; I.2.7 |
| url | https://arxiv.org/abs/2602.18907 |