用文字控制推荐结果，让推荐更透明可调。
Controllable and Content-Based Recommendations
- 基于用户文本画像生成推荐，通过文本瓶颈实现可控性。
- 在图像、音频、视频数据集上表现媲美传统模型。
- 支持用户用文字干预推荐方向，适合需要可解释性的场景。
传统推荐系统依赖隐含（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框架（CCBR），从文本用户画像构建推荐。CCBR可嵌入协同过滤模型，通过文本瓶颈实现可控性。不同于现有方法，CCBR直接从物品内容（图像、音频或视频）推断文本摘要。在图像、音频、视频数据集上，该框架性能与标准模型相当，同时提供可通过文本控制的推荐摘要。其表现优于近期基线TEARS。通过系统性干预实验，验证了用户导向机制的有效性。
原文摘要 · Abstract (English)
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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