Large language models (LLMs) have demonstrated success in various applications of user recommendation and personalization across e-commerce and entertainment. On many entertainment platforms such as Netflix, users typically interact with a wide range of titles, each represented by an artwork. Since users have diverse preferences, an artwork that appeals to one type of user may not resonate with another with different preferences. Given this user heterogeneity, our work explores the novel problem of personalized artwork recommendations according to diverse user preferences. Similar to the multi-dimensional nature of users' tastes, titles contain different themes and tones that may appeal to different viewers. For example, the same title might feature both heartfelt family drama and intense action scenes. Users who prefer romantic content may like the artwork emphasizing emotional warmth between the characters, while those who prefer action thrillers may find high-intensity action scenes more intriguing. Rather than a one-size-fits-all approach, we conduct post-training of pre-trained LLMs to make personalized artwork recommendations, selecting the most preferred visual representation of a title for each user and thereby improving user satisfaction and engagement. Our experimental results with Llama 3.1 8B models (trained on a dataset of 110K data points and evaluated on 5K held-out user-title pairs) show that the post-trained LLMs achieve 3-5\% improvements over the Netflix production model, suggesting a promising direction for granular personalized recommendations using LLMs.

翻译：大语言模型（LLMs）在电子商务和娱乐领域的用户推荐与个性化应用中已展现出显著成效。在Netflix等众多娱乐平台上，用户通常与大量影视作品进行交互，每部作品均以特定艺术作品（artwork）呈现。由于用户偏好存在多样性，吸引某一类用户的艺术作品可能无法引起具有不同偏好的其他用户的共鸣。针对这种用户异质性，本研究探索了一个新颖问题：如何根据多样化的用户偏好实现个性化艺术作品推荐。与用户品味的多维性相似，影视作品本身包含可能吸引不同观众的主题基调和情感色彩。例如，同一部作品可能同时包含温馨的家庭情感剧和激烈的动作场景。偏好浪漫内容的用户可能更青睐强调角色间情感温度的艺术作品，而偏好动作惊悚片的用户则可能对高强度动作场景更感兴趣。区别于“一刀切”的传统方法，本研究通过对预训练大语言模型进行后训练，实现个性化艺术作品推荐，为每位用户选择其最偏好的影视作品视觉呈现形式，从而提升用户满意度与参与度。基于Llama 3.1 8B模型的实验结果（使用11万数据点进行训练，并在5000个保留的用户-作品对上评估）表明，经过后训练的大语言模型相比Netflix生产模型实现了3-5%的性能提升，这为利用大语言模型实现细粒度个性化推荐提供了具有前景的研究方向。