Web: http://arxiv.org/abs/2205.02446

May 6, 2022, 1:11 a.m. | Fan Zhang, Qiuying Peng, Yulin Wu, Zheng Pan, Rong Zeng, Da Lin, Yue Qi

cs.LG updates on arXiv.org arxiv.org

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 …

ai arxiv graph online online video recommendation scale services video

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