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：用于生成高质量推荐的方法通常依赖于从交互数据中学习隐式表示。这些方法尽管性能优异，但并未提供用户控制其接收推荐内容的直接机制。本研究通过提出LACE——一种面向可控制文本推荐的新型概念值瓶颈模型——来解决该问题。LACE通过检索用户交互文档，为每个用户构建一组简洁的可读概念表示，并基于用户文档学习这些概念的个性化表征。随后利用这一基于概念的用户画像进行推荐。我们的模型设计允许用户通过透明用户画像的直观交互来控制推荐内容。我们首先在三个推荐任务（涵盖六个数据集的冷启动、热启动和零样本设置）的离线评估中验证了LACE的推荐质量。随后在模拟用户交互场景下验证了LACE的可控性。最后，我们将LACE实现为一个交互式可控制推荐系统，并通过用户研究表明：用户可通过与可编辑用户画像的交互，改善其接收到的推荐内容质量。