  In this paper, we consider controllability as a means to satisfy dynamic
preferences of users, enabling them to control recommendations such that their
current preference is met. While deep models have shown improved performance
for collaborative filtering, they are generally not amenable to fine grained
control by a user, leading to the development of methods like deep language
critiquing. We propose an alternate view, where instead of keyphrase based
critiques, a user is provided 'knobs' in a disentangled latent space, with each
knob corresponding to an item aspect. Disentanglement here refers to a latent
space where generative factors (here, a preference towards an item category
like genre) are captured independently in their respective dimensions, thereby
enabling predictable manipulations, otherwise not possible in an entangled
space. We propose using a (semi-)supervised disentanglement objective for this
purpose, as well as multiple metrics to evaluate the controllability and the
degree of personalization of controlled recommendations. We show that by
updating the disentangled latent space based on user feedback, and by
exploiting the generative nature of the recommender, controlled and
personalized recommendations can be produced. Through experiments on two widely
used collaborative filtering datasets, we demonstrate that a controllable
recommender can be trained with a slight reduction in recommender performance,
provided enough supervision is provided. The recommendations produced by these
models appear to both conform to a user's current preference and remain
personalized.
