Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services
Fan Zhang, Qiuying Peng, Yulin Wu, Zheng Pan, Rong Zeng, Da Lin, Yue, Qi

TL;DR
This paper introduces a multi-graph based multi-scenario recommendation framework that effectively integrates data across different scenarios to improve recommendation diversity, cold-start handling, and user engagement in large-scale online video services.
Contribution
It proposes a novel multi-graph structured data fusion approach for multi-scenario recommendation, enhancing cold-start performance and cross-scenario data utilization.
Findings
Increased CTR and video views per capita for new users.
Significant improvement in recommending cold and outer-scenario videos.
Outperforms baseline methods in online deployment metrics.
Abstract
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…
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