{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T07:11:03Z","timestamp":1779001863984,"version":"3.51.4"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030722395","type":"print"},{"value":"9783030722401","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-72240-1_10","type":"book-chapter","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T14:49:01Z","timestamp":1617288541000},"page":"134-149","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Federated Online Learning to Rank with Evolution Strategies: A Reproducibility Study"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4467-5574","authenticated-orcid":false,"given":"Shuyi","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6711-0955","authenticated-orcid":false,"given":"Shengyao","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0271-5563","authenticated-orcid":false,"given":"Guido","family":"Zuccon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"10_CR1","unstructured":"Chapelle, O., Chang, Y.: Yahoo! learning to rank challenge overview. In: Chapelle, O., Chang, Y., Liu, T. (eds.) Proceedings of the Yahoo! Learning to Rank Challenge, held at ICML 2010, Haifa, Israel, June 25, 2010. JMLR Proceedings, JMLR.org, vol. 14, pp. 1\u201324 (2011)"},{"issue":"4","key":"10_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1409220.1409222","volume":"2","author":"A Cooper","year":"2008","unstructured":"Cooper, A.: A survey of query log privacy-enhancing techniques from a policy perspective. ACM Trans. Web (TWEB) 2(4), 1\u201327 (2008)","journal-title":"ACM Trans. Web (TWEB)"},{"issue":"3\u20134","key":"10_CR3","first-page":"211","volume":"9","author":"C Dwork","year":"2014","unstructured":"Dwork, C., et al.: The algorithmic foundations of differential privacy. Found. Trend Theor. Comput. Sci. 9(3\u20134), 211\u2013407 (2014)","journal-title":"Found. Trend Theor. Comput. Sci."},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Guan, Z., Cutrell, E.: An eye tracking study of the effect of target rank on web search. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 417\u2013420 (2007)","DOI":"10.1145\/1240624.1240691"},{"key":"10_CR5","unstructured":"Guo, F., Liu, C., Wang, Y.M.: Efficient multiple-click models in web search. In: Proceedings of the Second International Conference on Web Search and Web Data Mining, WSDM 2009, Barcelona, Spain, February 9\u201311, 2009, pp. 124\u2013131. ACM (2009)"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Hofmann, K., Schuth, A., Whiteson, S., De Rijke, M.: Reusing historical interaction data for faster online learning to rank for IR. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 183\u2013192 (2013)","DOI":"10.1145\/2433396.2433419"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Jagerman, R., Oosterhuis, H., de Rijke, M.: To model or to intervene: a comparison of counterfactual and online learning to rank from user interactions. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 15\u201324 (2019)","DOI":"10.1145\/3331184.3331269"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133\u2013142 (2002)","DOI":"10.1145\/775047.775067"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Joachims, T., Swaminathan, A., Schnabel, T.: Unbiased learning-to-rank with biased feedback. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 781\u2013789 (2017)","DOI":"10.1145\/3018661.3018699"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Kharitonov, E.: Federated online learning to rank with evolution strategies. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 249\u2013257 (2019)","DOI":"10.1145\/3289600.3290968"},{"key":"10_CR11","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"10_CR12","unstructured":"Konecn\u00fd, J., McMahan, H.B., Ramage, D., Richt\u00e1rik, P.: Federated optimization: distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527 (2016)"},{"key":"10_CR13","unstructured":"Konecn\u00fd, J., McMahan, H.B., Yu, F.X., Richt\u00e1rik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Korolova, A., Kenthapadi, K., Mishra, N., Ntoulas, A.: Releasing search queries and clicks privately. In: Proceedings of the 18th International Conference on World Wide Web, pp. 171\u2013180 (2009)","DOI":"10.1145\/1526709.1526733"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Lefortier, D., Serdyukov, P., De Rijke, M.: Online exploration for detecting shifts in fresh intent. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 589\u2013598 (2014)","DOI":"10.1145\/2661829.2661947"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Oosterhuis, H., de Rijke, M.: Differentiable unbiased online learning to rank. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1293\u20131302 (2018)","DOI":"10.1145\/3269206.3271686"},{"issue":"3","key":"10_CR17","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1111\/j.1083-6101.2007.00351.x","volume":"12","author":"B Pan","year":"2007","unstructured":"Pan, B., Hembrooke, H., Joachims, T., Lorigo, L., Gay, G., Granka, L.: In google we trust: users\u2019 decisions on rank, position, and relevance. J. Comput. Mediated Commun. 12(3), 801\u2013823 (2007)","journal-title":"J. Comput. Mediated Commun."},{"key":"10_CR18","unstructured":"Qin, T., Liu, T.: Introducing LETOR 4.0 datasets. arXiv preprint arXiv:1306.2597 (2013)"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Radlinski, F., Kleinberg, R., Joachims, T.: Learning diverse rankings with multi-armed bandits. In: Proceedings of the 25th International Conference on Machine Learning, pp. 784\u2013791 (2008)","DOI":"10.1145\/1390156.1390255"},{"key":"10_CR20","unstructured":"Salimans, T., Ho, J., Chen, X., Sidor, S., Sutskever, I.: Evolution strategies as a scalable alternative to reinforcement learning. arXiv preprint arXiv:1703.03864 (2017)"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Sanderson, M.: Test collection based evaluation of information retrieval systems. Now Publishers Inc (2010)","DOI":"10.1561\/1500000009"},{"key":"10_CR22","unstructured":"Schuth, A., Oosterhuis, H., Whiteson, S., de Rijke, M.: Multileave gradient descent for fast online learning to rank. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, San Francisco, CA, USA, February 22\u201325, 2016, pp. 457\u2013466. ACM (2016)"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Wang, X., Bendersky, M., Metzler, D., Najork, M.: Learning to rank with selection bias in personal search. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 115\u2013124 (2016)","DOI":"10.1145\/2911451.2911537"},{"issue":"3\u20134","key":"10_CR24","first-page":"229","volume":"8","author":"RJ Williams","year":"1992","unstructured":"Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8(3\u20134), 229\u2013256 (1992)","journal-title":"Mach. Learn."},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Yang, G.H., Zhang, S.: Differential privacy for information retrieval. In: Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, pp. 325\u2013326 (2017)","DOI":"10.1145\/3121050.3121107"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Yang, H., Soboroff, I., Xiong, L., Clarke, C.L., Garfinkel, S.L.: Privacy-preserving IR 2016: differential privacy, search, and social media. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1247\u20131248 (2016)","DOI":"10.1145\/2911451.2917763"},{"issue":"2","key":"10_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1\u201319 (2019)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Yue, Y., Joachims, T.: Interactively optimizing information retrieval systems as a dueling bandits problem. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1201\u20131208 (2009)","DOI":"10.1145\/1553374.1553527"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, S., Yang, H., Singh, L.: Anonymizing query logs by differential privacy. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 753\u2013756 (2016)","DOI":"10.1145\/2911451.2914732"},{"key":"10_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/978-3-030-45439-5_28","volume-title":"Advances in Information Retrieval","author":"S Zhuang","year":"2020","unstructured":"Zhuang, S., Zuccon, G.: Counterfactual online learning to rank. In: Jose, J.M., et al. (eds.) ECIR 2020. LNCS, vol. 12035, pp. 415\u2013430. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-45439-5_28"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-72240-1_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T15:05:32Z","timestamp":1617289532000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-72240-1_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030722395","9783030722401"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72240-1_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 March 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 April 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"43","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ecir2021.eu\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"436","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":"50","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":"39","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":"11% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}