  With the increase of content pages and interactive buttons in online services
such as online-shopping and video-watching websites, industrial-scale
recommender systems face challenges in multi-domain and multi-task
recommendations. The core of multi-task and multi-domain recommendation is to
accurately capture user interests in multiple scenarios given multiple user
behaviors. In this paper, we propose a plug-and-play \textit{\textbf{P}arameter
and \textbf{E}mbedding \textbf{P}ersonalized \textbf{Net}work
(\textbf{PEPNet})} for multi-domain and multi-task recommendation. PEPNet takes
personalized prior information as input and dynamically scales the bottom-level
Embedding and top-level DNN hidden units through gate mechanisms.
\textit{Embedding Personalized Network (EPNet)} performs personalized selection
on Embedding to fuse features with different importance for different users in
multiple domains. \textit{Parameter Personalized Network (PPNet)} executes
personalized modification on DNN parameters to balance targets with different
sparsity for different users in multiple tasks. We have made a series of
special engineering optimizations combining the Kuaishou training framework and
the online deployment environment. By infusing personalized selection of
Embedding and personalized modification of DNN parameters, PEPNet tailored to
the interests of each individual obtains significant performance gains, with
online improvements exceeding 1\% in multiple task metrics across multiple
domains. We have deployed PEPNet in Kuaishou apps, serving over 300 million
users every day.
