Machine Learning Summits '20 - 2020 Agenda

Can we design recommenders that encourage user trajectories aligned with the true underlying user utilities? Besides engagement, user satisfaction and responsibility arise as important pillars of the recommendation problem. Motivated by this, we will discuss various efforts utilizing the reward function as an important lever of Reinforcement Learning (RL)-based recommenders, so to guide the model learning that for certain states (i.e., latent user representation at a certain point of the trajectory) certain actions (i.e., items to recommend) will bring higher user utility than others. We will also outline current and future directions on overcoming challenges of signals' sparsity and interplay among various reward signals. I am a Research Engineer at Google, leading several efforts on recommender systems and reinforcement learning in Google Brain.