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[2602.18907] DeepInterestGR: Mining Deep Multi-Interest Using Multi-Modal LLMs for Generative Recommendation
Summary
The paper presents DeepInterestGR, a novel framework that enhances generative recommendation systems by mining deep multi-interests using multi-modal large language models (LLMs). It addresses the limitations of existing methods by capturing richer user interests.
Why It Matters
As recommendation systems become increasingly central to user engagement, understanding and leveraging deep user interests can significantly enhance personalization and effectiveness. This research introduces innovative techniques that could lead to more accurate and interpretable recommendations, which is crucial for businesses relying on user data.
Key Takeaways
- DeepInterestGR uses multi-modal LLMs to extract deeper user interests.
- The framework addresses the 'Shallow Interest' problem in existing recommendation systems.
- It employs a two-stage training pipeline combining supervised fine-tuning and reinforcement learning.
- Experiments show significant performance improvements over state-of-the-art methods.
- The approach enhances both personalization depth and recommendation interpretability.
Computer Science > Machine Learning arXiv:2602.18907 (cs) [Submitted on 21 Feb 2026] Title:DeepInterestGR: Mining Deep Multi-Interest Using Multi-Modal LLMs for Generative Recommendation Authors:Yangchen Zeng View a PDF of the paper titled DeepInterestGR: Mining Deep Multi-Interest Using Multi-Modal LLMs for Generative Recommendation, by Yangchen Zeng View PDF HTML (experimental) Abstract:Recent generative recommendation frameworks have demonstrated remarkable scaling potential by reformulating item prediction as autoregressive Semantic ID (SID) generation. However, existing methods primarily rely on shallow behavioral signals, encoding items solely through surface-level textual features such as titles and descriptions. This reliance results in a critical Shallow Interest problem: the model fails to capture the latent, semantically rich interests underlying user interactions, limiting both personalization depth and recommendation interpretability. DeepInterestGR introduces three key innovations: (1) Multi-LLM Interest Mining (MLIM): We leverage multiple frontier LLMs along with their multi-modal variants to extract deep textual and visual interest representations through Chain-of-Thought prompting. (2) Reward-Labeled Deep Interest (RLDI): We employ a lightweight binary classifier to assign reward labels to mined interests, enabling effective supervision signals for reinforcement learning. (3) Interest-Enhanced Item Discretization (IEID): The curated deep interests are enco...
