生成而非推荐：个性化多模态内容生成
Generate, Not Recommend: Personalized Multimodal Content Generation
AI总结：
本文提出用大型多模态模型直接为个体用户生成个性化多模态内容的新范式，结合监督微调与在线强化学习，实验和用户研究表明生成结果既符合历史偏好又关联未来兴趣。
摘要：
为了解决海量网络内容带来的信息过载挑战，推荐系统被广泛应用于检索并向用户呈现个性化结果。然而，推荐任务本质上受限于对已有条目的过滤，缺乏生成新颖概念的能力，这限制了它们充分满足用户需求和偏好的能力。在本文中，我们提出了一种超越内容过滤与选择的新范式：直接以多模态形式（如图像）为个体用户生成个性化条目。为了实现这一目标，我们利用任意到任意的大型多模态模型（LMMs），并通过监督微调和在线强化学习策略对其进行训练，使其具备为用户生成定制化下一项内容的能力。在两个基准数据集上的实验以及用户研究证实了所提方法的有效性。值得注意的是，生成的图像不仅与用户的历史偏好高度一致，还展现出与其潜在未来兴趣的相关性。
英文摘要：
To address the challenge of information overload from massive web contents, recommender systems are widely applied to retrieve and present personalized results for users. However, recommendation tasks are inherently constrained to filtering existing items and lack the ability to generate novel concepts, limiting their capacity to fully satisfy user demands and preferences. In this paper, we propose a new paradigm that goes beyond content filtering and selecting: directly generating personalized items in a multimodal form, such as images, tailored to individual users. To accomplish this, we leverage any-to-any Large Multimodal Models (LMMs) and train them in both supervised fine-tuning and online reinforcement learning strategy to equip them with the ability to yield tailored next items for users. Experiments on two benchmark datasets and user study confirm the efficacy of the proposed method. Notably, the generated images not only align well with users' historical preferences but also exhibit relevance to their potential future interests.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
