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 encoded into semantic embeddings and quantized into SID tokens via RQ-VAE. We adopt a two-stage training pipeline: supervised fine-tuning aligns the generative model with deep interest signals and collaborative filtering patterns, followed by reinforcement learning with GRPO optimized by our Interest-Aware Reward. Experiments on three Amazon Review benchmarks demonstrate that DeepInterestGR consistently outperforms state-of-the-art baselines across HR@K and NDCG@K metrics.

翻译：近期生成式推荐框架通过将物品预测重构为自回归语义ID生成任务，展现了卓越的扩展潜力。然而，现有方法主要依赖浅层行为信号，仅通过标题和描述等表层文本特征对物品进行编码。这种依赖导致了一个关键的浅层兴趣问题：模型无法捕捉用户交互背后潜在的、语义丰富的兴趣，限制了推荐的个性化深度与可解释性。DeepInterestGR提出三项核心创新：(1) 多LLM兴趣挖掘：我们利用多种前沿大语言模型及其多模态变体，通过思维链提示技术提取深层文本与视觉兴趣表征。(2) 奖励标注的深层兴趣：采用轻量级二分类器为挖掘的兴趣分配奖励标签，为强化学习提供有效的监督信号。(3) 兴趣增强的物品离散化：将提炼的深层兴趣编码为语义嵌入，并通过RQ-VAE量化为语义ID令牌。我们采用两阶段训练流程：监督微调阶段使生成模型与深层兴趣信号及协同过滤模式对齐，随后通过我们设计的兴趣感知奖励函数优化的GRPO算法进行强化学习。在三个亚马逊评论基准数据集上的实验表明，DeepInterestGR在HR@K与NDCG@K指标上均持续超越现有最优基线方法。