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.
翻译:最近,工业建议服务因不断更新深层学习方法而得到加强。然而,工业建议服务仍面临一些不利的挑战,如暴露偏差和冷开始问题,因为人类互动历史的机器学习培训循环导致算法反复建议暴露项目,而忽略不那么活跃的项目。在多设想平台上还存在其他问题,例如从附属假设中适当整合数据,我们观察到,可以通过通过传递信息的方式通过图形结构化数据集成来缓解这些问题。在本文中,我们提出了一个多图表结构化多设想建议解决方案,其中将互动数据与多图谱组合在一起,并通过图表学习获得代表性。在现实世界数据集上进行了广泛的离线和在线实验,其中拟议方法显示,新用户在部署的基线集方面人均增长0.63%和0.71%,在将外部访问视频数量增加25%方面超过了常规方法,视频监视增加了116%,证实其在激活冷视频和充实目标建议方面的优势。