PEPNet：注入个性化先验信息的参数与嵌入个性化网络
PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information
AI总结：
本文提出即插即用的PEPNet，通过EPNet个性化选择Embedding和PPNet个性化修改DNN参数，解决多领域多任务推荐问题，在Kuaishou部署中实现多指标超1%提升并服务超3亿用户。
摘要：
随着在线购物和视频观看等在线服务中内容页面和交互按钮的增加，工业规模的推荐系统在多领域和多任务推荐中面临挑战。多任务和多领域推荐的核心是在给定多种用户行为的情况下，准确捕捉多个场景中的用户兴趣。在本文中，我们提出了一种即插即用的参数与嵌入个性化网络（PEPNet），用于多领域和多任务推荐。PEPNet将个性化先验信息作为输入，并通过门控机制动态调整底层Embedding和顶层DNN隐藏单元。嵌入个性化网络（EPNet）对Embedding执行个性化选择，以在多个领域为不同用户融合具有不同重要性的特征。参数个性化网络（PPNet）对DNN参数执行个性化修改，以在多个任务中为不同用户平衡具有不同稀疏性的目标。我们结合Kuaishou训练框架和在线部署环境，进行了一系列特殊的工程优化。通过注入Embedding的个性化选择和DNN参数的个性化修改，为每个个体的兴趣量身定制的PEPNet获得了显著的性能提升，在多个领域的多个任务指标上在线改进超过1%。我们已将PEPNet部署在Kuaishou应用中，每天服务超过3亿用户。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
