Video Recommendation with Multi-gate Mixture of Experts Soft Actor Critic

Dingcheng Li, Xu Li, Jun Wang, Ping Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

In this paper, we propose a reinforcement learning based large scale multi-objective ranking system for optimizing short-video recommendation on an industrial video sharing platform. Multiple competing ranking objective and implicit selection bias in user feedback are the main challenges in real-world platform. In order to address those challenges, we integrate multi-gate mixture of experts and soft actor critic into the ranking system. We demonstrated that our proposed framework can greatly reduce the loss function compared with systems only based on single strategies.

Original languageEnglish (US)
Title of host publicationSIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages1553-1556
Number of pages4
ISBN (Electronic)9781450380164
DOIs
StatePublished - Jul 25 2020
Externally publishedYes
Event43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020 - Virtual, Online, China
Duration: Jul 25 2020Jul 30 2020

Publication series

NameSIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020
Country/TerritoryChina
CityVirtual, Online
Period7/25/207/30/20

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Information Systems
  • Software

Keywords

  • multi-gate mixture experts
  • reinforcement learning
  • soft actor critic
  • video recommendation

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