{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T16:55:55Z","timestamp":1767372955006,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031306716"},{"type":"electronic","value":"9783031306723"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-30672-3_4","type":"book-chapter","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T11:10:49Z","timestamp":1681384249000},"page":"54-63","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Thompson Sampling with\u00a0Time-Varying Reward for\u00a0Contextual Bandits"],"prefix":"10.1007","author":[{"given":"Cairong","family":"Yan","sequence":"first","affiliation":[]},{"given":"Hualu","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Haixia","family":"Han","sequence":"additional","affiliation":[]},{"given":"Yanting","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zijian","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"4_CR1","unstructured":"Agrawal, S., Goyal, N.: Thompson sampling for contextual bandits with linear payoffs. In: Proceedings of the 30th International Conference on Machine Learning, pp. 127\u2013135 (2013)"},{"key":"4_CR2","unstructured":"Auer, P., Cesa-Bianchi, N., Freund, Y., Schapire, R.E.: Gambling in a rigged casino: the adversarial multi-armed bandit problem. In: Proceedings of the 36th Annual Foundations of Computer Science, pp. 322\u2013331 (1995)"},{"key":"4_CR3","unstructured":"Besbes, O., Gur, Y., Zeevi, A.: Stochastic multi-armed-bandit problem with non-stationary rewards. In: Proceedings of the 28th Conference on Neural Information Processing Systems, pp. 199\u2013207 (2014)"},{"key":"4_CR4","doi-asserted-by":"crossref","unstructured":"Cheung, W.C., Simchi-Levi, D., Zhu, R.: Learning to optimize under non-stationarity. In: Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, pp. 1079\u20131087 (2019)","DOI":"10.2139\/ssrn.3261050"},{"key":"4_CR5","unstructured":"Deng, Y., Zhou, X., Kim, B., Tewari, A., Gupta, A., Shroff, N.: Weighted gaussian process bandits for non-stationary environments. In: Proceedings of the International Conference on Artificial Intelligence and Statistics, pp. 6909\u20136932 (2022)"},{"issue":"10","key":"4_CR6","doi-asserted-by":"publisher","first-page":"1670","DOI":"10.1109\/TC.2020.3022634","volume":"70","author":"G Ghatak","year":"2020","unstructured":"Ghatak, G.: A change-detection-based thompson sampling framework for non-stationary bandits. IEEE Trans. Comput. 70(10), 1670\u20131676 (2020)","journal-title":"IEEE Trans. Comput."},{"key":"4_CR7","unstructured":"Li, C., Wang, H.: Asynchronous upper confidence bound algorithms for federated linear bandits. In: Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, pp. 6529\u20136553 (2022)"},{"key":"4_CR8","unstructured":"Liu, E.Z., Raghunathan, A., Liang, P., Finn, C.: Decoupling exploration and exploitation for meta-reinforcement learning without sacrifices. In: Proceedings of the 38th International Conference on Machine Learning, pp. 6925\u20136935 (2021)"},{"key":"4_CR9","unstructured":"Russac, Y., Vernade, C., Capp\u00e9, O.: Weighted linear bandits for non-stationary environments. In: Advances in Neural Information Processing Systems (2019)"},{"key":"4_CR10","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1613\/jair.1.11407","volume":"68","author":"F Trovo","year":"2020","unstructured":"Trovo, F., Paladino, S., Restelli, M., Gatti, N.: Sliding-window thompson sampling for non-stationary settings. J. Artif. Intell. Res. 68, 311\u2013364 (2020)","journal-title":"J. Artif. Intell. Res."},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Vakili, S., Zhao, Q., Zhou, Y.: Time-varying stochastic multi-armed bandit problems. In: Proceedings of the 48th Asilomar Conference on Signals, Systems and Computers, pp. 2103\u20132107 (2014)","DOI":"10.1109\/ACSSC.2014.7094845"},{"issue":"2","key":"4_CR12","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1109\/TIFS.2016.2611487","volume":"12","author":"L Xu","year":"2016","unstructured":"Xu, L., Jiang, C., Qian, Y., Zhao, Y., Li, J., Ren, Y.: Dynamic privacy pricing: a multi-armed bandit approach with time-variant rewards. IEEE Trans. Inf. Forensics Secur. 12(2), 271\u2013285 (2016)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Xu, X., Dong, F., Li, Y., He, S., Li, X.: Contextual-bandit based personalized recommendation with time-varying user interests. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence, pp. 6518\u20136525 (2020)","DOI":"10.1609\/aaai.v34i04.6125"},{"key":"4_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109927","volume":"257","author":"C Yan","year":"2022","unstructured":"Yan, C., Han, H., Zhang, Y., Zhu, D., Wan, Y.: Dynamic clustering based contextual combinatorial multi-armed bandit for online recommendation. Knowl.-Based Syst. 257, 109927 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"4_CR15","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.neucom.2020.01.086","volume":"399","author":"Z Zhu","year":"2020","unstructured":"Zhu, Z., Huang, L., Xu, H.: Self-accelerated thompson sampling with near-optimal regret upper bound. Neurocomputing 399, 37\u201347 (2020)","journal-title":"Neurocomputing"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30672-3_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T17:20:43Z","timestamp":1710264043000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30672-3_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031306716","9783031306723"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30672-3_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"14 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.tjudb.cn\/dasfaa2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"652","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"125","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"66","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7.3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}