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

PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information