Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services
It addresses cold-start and data fusion problems for large-scale online video services, representing an incremental improvement over existing methods.
The paper tackles de-biasing challenges like exposure bias and cold-start in multi-scenario recommendation systems by proposing a multi-graph structured solution, resulting in a 0.63% and 0.71% increase in CTR and Video Views per capita for new users, and a 25% and 116% boost in outer-scenario videos and watches.
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.