AI 中文总结
研究针对传统推荐系统难以解释和控制的问题，提出CCBR框架，基于文本用户简档表示构建推荐，通过文本瓶颈引入可控性，能实现多模态干预，在多数据集上有竞争力且优于基线，证明了用户引导机制有效。
AI 中文摘要
传统推荐系统依赖潜在（密集）表示，难以解释和控制。我们提出可控且基于内容的推荐（CCBR）框架，它基于文本用户简档表示构建推荐。CCBR插入协同过滤模型并通过文本瓶颈引入可控性。我们表明CCBR实现基于文本和多模态干预，让用户引导模型走向其偏好方向。与现有可控推荐系统不同，CCBR直接从项目内容（图像、音频或视频）推断文本摘要。在基于图像、音频和视频的数据集上，该框架在提供可控文本摘要时，与标准（潜在表示）模型有竞争力，且优于近期基线TEARS。通过系统干预证明了用户引导机制的有效性。
英文摘要
Traditional recommendation systems rely on latent (dense) representations, making them difficult to interpret and control. We propose the Controllable and Content-Based Recommendations (CCBR) framework, which builds its recommendations from textual user profile representations. CCBR plugs into collaborative filtering models and introduces controllability via text bottlenecks. We show that CCBR enables text-based and multimodal interventions, allowing users to steer the model towards the directions they prefer. Different from existing controllable recommendation systems, CCBR infers the text summaries directly from item contents (images, audio or video). Across image-, audio-, and video-based datasets, we demonstrate that the proposed framework obtains competitive model performance with standard (latent-representation) models while providing controllable model summaries via text. The model also outperforms TEARS, a recent baseline for controllable recommendation systems. Through systematic interventions, we demonstrate the efficacy of the user steering mechanism.
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