Methods for making high-quality recommendations often rely on learning latent representations from interaction data. These methods, while performant, do not provide ready mechanisms for users to control the recommendation they receive. Our work tackles this problem by proposing LACE, a novel concept value bottleneck model for controllable text recommendations. LACE represents each user with a succinct set of human-readable concepts through retrieval given user-interacted documents and learns personalized representations of the concepts based on user documents. This concept based user profile is then leveraged to make recommendations. The design of our model affords control over the recommendations through a number of intuitive interactions with a transparent user profile. We first establish the quality of recommendations obtained from LACE in an offline evaluation on three recommendation tasks spanning six datasets in warm-start, cold-start, and zero-shot setups. Next, we validate the controllability of LACE under simulated user interactions. Finally, we implement LACE in an interactive controllable recommender system and conduct a user study to demonstrate that users are able to improve the quality of recommendations they receive through interactions with an editable user profile.

翻译：高精度推荐方法通常依赖从交互数据中学习潜在表示。这类方法虽然性能优异，但缺乏用户直接控制推荐结果的机制。本研究提出LACE模型——一种面向可控文本推荐的新型概念价值瓶颈模型。LACE通过检索用户交互文档，以简洁的人类可读概念集为每位用户建立画像，并基于用户文档学习概念的个性化表示。该概念驱动型用户画像随后被用于生成推荐。模型设计通过透明化用户画像的直观交互方式实现推荐可控性。我们首先在六个数据集（涵盖热启动、冷启动和零样本三种场景）的三个推荐任务上进行离线评估，验证LACE的推荐质量；其次通过模拟用户交互验证其可控性；最终将LACE部署为交互式可控推荐系统并开展用户研究，实验证明用户可通过编辑用户画像交互优化推荐质量。