The integration of Large Language Models (LLMs) into recommender systems has led to substantial performance improvements. However, this often comes at the cost of diminished recommendation diversity, which can negatively impact user satisfaction. To address this issue, controllable recommendation has emerged as a promising approach, allowing users to specify their preferences and receive recommendations that meet their diverse needs. Despite its potential, existing controllable recommender systems frequently rely on simplistic mechanisms, such as a single prompt, to regulate diversity-an approach that falls short of capturing the full complexity of user preferences. In response to these limitations, we propose DLCRec, a novel framework designed to enable fine-grained control over diversity in LLM-based recommendations. Unlike traditional methods, DLCRec adopts a fine-grained task decomposition strategy, breaking down the recommendation process into three sequential sub-tasks: genre prediction, genre filling, and item prediction. These sub-tasks are trained independently and inferred sequentially according to user-defined control numbers, ensuring more precise control over diversity. Furthermore, the scarcity and uneven distribution of diversity-related user behavior data pose significant challenges for fine-tuning. To overcome these obstacles, we introduce two data augmentation techniques that enhance the model's robustness to noisy and out-of-distribution data. These techniques expose the model to a broader range of patterns, improving its adaptability in generating recommendations with varying levels of diversity. Our extensive empirical evaluation demonstrates that DLCRec not only provides precise control over diversity but also outperforms state-of-the-art baselines across multiple recommendation scenarios.

翻译：将大型语言模型（LLM）集成到推荐系统中已带来显著的性能提升。然而，这通常以牺牲推荐多样性为代价，可能对用户满意度产生负面影响。为解决这一问题，可控推荐已成为一种有前景的方法，允许用户指定其偏好并接收满足其多样化需求的推荐。尽管潜力巨大，现有可控推荐系统常依赖简单机制（如单一提示）来调节多样性——这种方法难以捕捉用户偏好的全部复杂性。针对这些局限性，我们提出了DLCRec，一个旨在实现对基于LLM推荐中多样性进行细粒度控制的新框架。与传统方法不同，DLCRec采用细粒度任务分解策略，将推荐过程分解为三个顺序子任务：类型预测、类型填充和项目预测。这些子任务独立训练，并根据用户定义的控制参数顺序推断，从而确保对多样性更精确的控制。此外，多样性相关用户行为数据的稀缺性和分布不均对微调构成重大挑战。为克服这些障碍，我们引入了两种数据增强技术，以增强模型对噪声数据和分布外数据的鲁棒性。这些技术使模型接触更广泛的模式，提高了其在生成具有不同多样性水平的推荐时的适应性。我们广泛的实证评估表明，DLCRec不仅提供了对多样性的精确控制，而且在多种推荐场景中超越了现有最先进的基线方法。