  In an era of countless content offerings, recommender systems alleviate
information overload by providing users with personalized content suggestions.
Due to the scarcity of explicit user feedback, modern recommender systems
typically optimize for the same fixed combination of implicit feedback signals
across all users. However, this approach disregards a growing body of work
highlighting that (i) implicit signals can be used by users in diverse ways,
signaling anything from satisfaction to active dislike, and (ii) different
users communicate preferences in different ways. We propose applying the recent
Interaction Grounded Learning (IGL) paradigm to address the challenge of
learning representations of diverse user communication modalities. Rather than
requiring a fixed, human-designed reward function, IGL is able to learn
personalized reward functions for different users and then optimize directly
for the latent user satisfaction. We demonstrate the success of IGL with
experiments using simulations as well as with real-world production traces.
