  With the explosive growth of online products and content, recommendation
techniques have been considered as an effective tool to overcome information
overload, improve user experience, and boost business revenue. In recent years,
we have observed a new desideratum of considering long-term rewards of multiple
related recommendation tasks simultaneously. The consideration of long-term
rewards is strongly tied to business revenue and growth. Learning multiple
tasks simultaneously could generally improve the performance of individual task
due to knowledge sharing in multi-task learning. While a few existing works
have studied long-term rewards in recommendations, they mainly focus on a
single recommendation task. In this paper, we propose {\it PoDiRe}: a
\underline{po}licy \underline{di}stilled \underline{re}commender that can
address long-term rewards of recommendations and simultaneously handle multiple
recommendation tasks. This novel recommendation solution is based on a marriage
of deep reinforcement learning and knowledge distillation techniques, which is
able to establish knowledge sharing among different tasks and reduce the size
of a learning model. The resulting model is expected to attain better
performance and lower response latency for real-time recommendation services.
In collaboration with Samsung Game Launcher, one of the world's largest
commercial mobile game platforms, we conduct a comprehensive experimental study
on large-scale real data with hundreds of millions of events and show that our
solution outperforms many state-of-the-art methods in terms of several standard
evaluation metrics.
