Recommending What Video to Watch Next: A Multitask Ranking System

Aditee Ajit Kumthekar
Aniruddh Nath
Jilin Chen
Li Wei
Lichan Hong
Mahesh Sathiamoorthy
Shawn Andrews
Zhe Zhao
Recsys 2019(2019)
Google Scholar


In this paper, we introduce a large scale multi-objective ranking system for recommending what video to watch next on an industrial video sharing platform. The system faces many real-world challenges, including the presence of multiple competing ranking objectives, as well as implicit selection biases in user feedback. To tackle these challenges, we explored a variety of soft-parameter sharing techniques such as Multi-gate Mixture-of-Experts so as to efficiently optimize for multiple ranking objectives. Additionally, we mitigated the selection biases by adopting a Wide & Deep frame- work. We demonstrated that our proposed techniques can lead to substantial improvements on recommendation quality on one of the world’s largest video sharing platforms.