{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T12:15:18Z","timestamp":1773317718479,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":22,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"GuangDong Basic and Applied Basic Research Foundation","award":["2022A1515010800"],"award-info":[{"award-number":["2022A1515010800"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539081","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"4381-4390","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["A Stochastic Shortest Path Algorithm for Optimizing Spaced Repetition Scheduling"],"prefix":"10.1145","author":[{"given":"Junyao","family":"Ye","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, Shenzhen, China"}]},{"given":"Jingyong","family":"Su","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, Shenzhen, China"}]},{"given":"Yilong","family":"Cao","sequence":"additional","affiliation":[{"name":"MaiMemo Inc., Qingyuan, China"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1037\/0033-295X.111.4.1036"},{"key":"e_1_3_2_2_2_1","first-page":"35","article-title":"A New Theory of Disuse and an Old Theory of Stimulus Fluctuation. From learning processes to cognitive processes: Essays in honor of William K","volume":"2","author":"Bjork Robert A.","year":"1992","unstructured":"Robert A. Bjork, Elizabeth L. Bjork, et al. 1992. A New Theory of Disuse and an Old Theory of Stimulus Fluctuation. From learning processes to cognitive processes: Essays in honor of William K. Estes, Vol. 2 (1992), 35--67.","journal-title":"Estes"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/acp.1507"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1037\/0033-2909.132.3.354"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9280.2008.02209.x"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1037\/10011-000"},{"key":"e_1_3_2_2_7_1","unstructured":"Anette Hunziker Yuxin Chen et al. 2019. Teaching Multiple Concepts to a Forgetful Learner. In Advances in Neural Information Processing Systems. 4050--4060."},{"key":"e_1_3_2_2_8_1","volume-title":"So lernt man leben. Droemer-Knaur","author":"Leitner Sebastian","unstructured":"Sebastian Leitner. 1974. So lernt man leben. Droemer-Knaur, M\u00fcnchen, Z\u00fcrich."},{"key":"e_1_3_2_2_9_1","volume-title":"Artificial Intelligence in Education","author":"Maass Jaclyn K.","unstructured":"Jaclyn K. Maass, Philip I. Pavlik, and Henry Hua. 2015. How Spacing and Variable Retrieval Practice Affect the Learning of Statistics Concepts. In Artificial Intelligence in Education. Springer, 247--256."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-5371(70)80107-4"},{"key":"e_1_3_2_2_11_1","volume-title":"Vul, and Michael C Mozer","author":"Pashler Harold","year":"2009","unstructured":"Harold Pashler, Nicholas Cepeda, Robert V Lindsey, Ed Vul, and Michael C Mozer. 2009. Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory. In Advances in Neural Information Processing Systems. 1321--1329."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939850"},{"key":"e_1_3_2_2_13_1","volume-title":"Accelerating Human Learning with Deep Reinforcement Learning","author":"Reddy Siddharth","year":"2017","unstructured":"Siddharth Reddy, Sergey Levine, and Anca Dragan. 2017. Accelerating Human Learning with Deep Reinforcement Learning. University of California, Berkeley (2017), 9."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1174"},{"key":"e_1_3_2_2_15_1","volume-title":"Using Deep Reinforcement Learning for Personalizing Review Sessions on E-Learning Platforms with Spaced Repetition. Ph.,D. Dissertation","author":"Sinha Sugandh","unstructured":"Sugandh Sinha. 2019. Using Deep Reinforcement Learning for Personalizing Review Sessions on E-Learning Platforms with Spaced Repetition. Ph.,D. Dissertation. KTH Royal Institute of Technology."},{"key":"e_1_3_2_2_16_1","first-page":"77","article-title":"The Right Time to Learn: Mechanisms and Optimization of Spaced Learning. Nature reviews","volume":"17","author":"Smolen Paul","year":"2016","unstructured":"Paul Smolen, Yili Zhang, and John H. Byrne. 2016. The Right Time to Learn: Mechanisms and Optimization of Spaced Learning. Nature reviews. Neuroscience, Vol. 17, 2 (2016), 77--88.","journal-title":"Neuroscience"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1815156116"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjqs-2011-000702"},{"key":"e_1_3_2_2_19_1","unstructured":"Utkarsh Upadhyay Abir De and Manuel Gomez-Rodrizuez. 2018. Deep Reinforcement Learning of Marked Temporal Point Processes. In Advances in Neural Information Processing Systems. 3172--3182."},{"key":"e_1_3_2_2_20_1","unstructured":"Piotr A. Wozniak. 1990. Optimization of Learning. http:\/\/super-memory.com\/english\/ol.htm."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401316"},{"key":"e_1_3_2_2_22_1","volume-title":"Artificial Intelligence in Education","author":"Zaidi Ahmed","unstructured":"Ahmed Zaidi, Andrew Caines, Russell Moore, Paula Buttery, and Andrew Rice. 2020. Adaptive Forgetting Curves for Spaced Repetition Language Learning. In Artificial Intelligence in Education. Springer, 358--363."}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539081","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539081","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:51Z","timestamp":1750183791000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539081"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":22,"alternative-id":["10.1145\/3534678.3539081","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539081","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}