  The last decade has witnessed many successes of deep learning-based models
for industry-scale recommender systems. These models are typically trained
offline in a batch manner. While being effective in capturing users' past
interactions with recommendation platforms, batch learning suffers from long
model-update latency and is vulnerable to system biases, making it hard to
adapt to distribution shift and explore new items or user interests. Although
online learning-based approaches (e.g., multi-armed bandits) have demonstrated
promising theoretical results in tackling these challenges, their practical
real-time implementation in large-scale recommender systems remains limited.
First, the scalability of online approaches in servicing a massive online
traffic while ensuring timely updates of bandit parameters poses a significant
challenge. Additionally, exploring uncertainty in recommender systems can
easily result in unfavorable user experience, highlighting the need for
devising intricate strategies that effectively balance the trade-off between
exploitation and exploration. In this paper, we introduce Online Matching: a
scalable closed-loop bandit system learning from users' direct feedback on
items in real time. We present a hybrid "offline + online" approach for
constructing this system, accompanied by a comprehensive exposition of the
end-to-end system architecture. We propose Diag-LinUCB -- a novel extension of
the LinUCB algorithm -- to enable distributed updates of bandits parameter in a
scalable and timely manner. We conduct live experiments in YouTube and show
that Online Matching is able to enhance the capabilities of fresh content
discovery and item exploration in the present platform.
