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：现有高质量推荐方法通常依赖从交互数据中学习隐表示。这类方法虽然性能卓越，但缺乏让用户控制推荐结果的现成机制。针对该问题，本文提出LACE——一种面向可控文本推荐的新型概念值瓶颈模型。LACE通过检索用户交互文档，将每位用户表示为一组简洁的可读概念，并基于用户文档学习概念的个性化表征，进而利用这种基于概念的用户画像进行推荐。模型设计允许用户通过透明用户画像的多种直观交互操作来控制推荐结果。我们首先在三个推荐任务（涵盖暖启动、冷启动和零样本六类数据集）的离线评估中验证LACE的推荐质量；随后在模拟用户交互场景下验证LACE的可控性；最后将LACE部署于可交互式可控推荐系统，通过用户研究证明用户可通过编辑用户画像交互操作提升获取推荐的质量。