Towards Companion Recommendation Systems

Recommendation systems can be seen as one of the first successful paradigms of true human-AI collaboration. That is, the AI identifies what the user might want and provide this to them at the right time; and the user, implicitly or explicitly, gives feedback of whether they value said recommendations. However, to make the recommender a \emph{true companion} of users, amplifying and augmenting the capabilities of users to be more knowledgeable, healthy, and happy, requires a shift into the way this collaboration happens. In this position paper, we argue for an increasing focus into reflecting the user values into the design, evaluation, training objectives, and interaction paradigm of state-of-the-art recommendation.