  Recently, industrial recommendation services have been boosted by the
continual upgrade of deep learning methods. However, they still face de-biasing
challenges such as exposure bias and cold-start problem, where circulations of
machine learning training on human interaction history leads algorithms to
repeatedly suggest exposed items while ignoring less-active ones. Additional
problems exist in multi-scenario platforms, e.g. appropriate data fusion from
subsidiary scenarios, which we observe could be alleviated through graph
structured data integration via message passing.
  In this paper, we present a multi-graph structured multi-scenario
recommendation solution, which encapsulates interaction data across scenarios
with multi-graph and obtains representation via graph learning. Extensive
offline and online experiments on real-world datasets are conducted where the
proposed method demonstrates an increase of 0.63% and 0.71% in CTR and Video
Views per capita on new users over deployed set of baselines and outperforms
regular method in increasing the number of outer-scenario videos by 25% and
video watches by 116%, validating its superiority in activating cold videos and
enriching target recommendation.
