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An industrial recommender system generally presents a hybrid list thatcontains results from multiple subsystems. In practice, each subsystem isoptimized with its own feedback data to avoid the disturbance among differentsubsystems. However, we argue that such data usage may lead to sub-optimalonline performance because of the \textit{data sparsity}. To alleviate thisissue, we propose to extract knowledge from the \textit{super-domain} thatcontains web-scale and long-time impression data, and further assist the onlinerecommendation task (downstream task). To this end, we propose a novelindustrial \textbf{K}nowl\textbf{E}dge \textbf{E}xtraction and\textbf{P}lugging (\textbf{KEEP}) framework, which is a two-stage frameworkthat consists of 1) a supervised pre-training knowledge extraction module onsuper-domain, and 2) a plug-in network that incorporates the extractedknowledge into the downstream model. This makes it friendly for incrementaltraining of online recommendation. Moreover, we design an efficient empiricalapproach for KEEP and introduce our hands-on experience during theimplementation of KEEP in a large-scale industrial system. Experimentsconducted on two real-world datasets demonstrate that KEEP can achievepromising results. It is notable that KEEP has also been deployed on thedisplay advertising system in Alibaba, bringing a lift of $+5.4\%$ CTR and$+4.7\%$ RPM.

KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging