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A Stochastic Shortest Path Algorithm for Optimizing Spaced Repetition Scheduling

  • Harbin Institute of Technology Shenzhen
  • MaiMemo Inc.

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Spaced repetition is a mnemonic technique where long-term memory can be efficiently formed by following review schedules. For greater memorization efficiency, spaced repetition schedulers need to model students' long-term memory and optimize the review cost. We have collected 220 million students' memory behavior logs with time-series features and built a memory model with Markov property. Based on the model, we design a spaced repetition scheduler guaranteed to minimize the review cost by a stochastic shortest path algorithm. Experimental results have shown a 12.6% performance improvement over the state-of-the-art methods. The scheduler has been successfully deployed in the online language-learning app MaiMemo to help millions of students.

Original languageEnglish
Title of host publicationKDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages4381-4390
Number of pages10
ISBN (Electronic)9781450393850
DOIs
StatePublished - 14 Aug 2022
Event28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 - Washington, United States
Duration: 14 Aug 202218 Aug 2022

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
Country/TerritoryUnited States
CityWashington
Period14/08/2218/08/22

Keywords

  • language learning
  • optimal control
  • spaced repetition

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