Sai Zhang
Dr. Sai Zhang is a software engineer at Google in Kirkland, Washington, where he leads a team to build configuration management platform and product for Cloud customers. He is broadly interested in programming languages, software systems, software engineering, and security. He has a PhD from the University of Washington where he worked on a variety of research topics related to program analysis, programming systems, developer tools, and human computer interaction.
Authored Publications
Google Publications
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Reward Shaping for User Satisfaction in a REINFORCE Recommender
Can Xu
Sriraj Badam
Trevor Potter
Daniel Li
Hao Wan
Elaine Le
Chris Berg
Eric Bencomo Dixon
(2021)
Preview abstract
How might we design Reinforcement Learning (RL)-based recommenders that
encourage aligning user trajectories with the underlying user satisfaction?
Three research questions are key: (1) measuring user satisfaction, (2)
combatting sparsity of satisfaction signals, and (3) adapting the training of
the recommender agent to maximize satisfaction. For measurement, it has been
found that surveys explicitly asking users to rate their experience with
consumed items can provide valuable orthogonal information to the
engagement/interaction data, acting as a proxy to the underlying user
satisfaction. For sparsity, i.e, only being able to observe how satisfied users
are with a tiny fraction of user-item interactions, imputation models can be
useful in predicting satisfaction level for all items users have consumed. For
learning satisfying recommender policies, we postulate that reward shaping in
RL recommender agents is powerful for driving satisfying user experiences.
Putting everything together, we propose to jointly learn a policy network and a
satisfaction imputation network: The role of the imputation network is to learn
which actions are satisfying to the user; while the policy network, built on
top of REINFORCE, decides which items to recommend, with the reward utilizing
the imputed satisfaction. We use both offline analysis and live experiments in
an industrial large-scale recommendation platform to demonstrate the promise of
our approach for satisfying user experiences.
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