  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.
