{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T12:17:52Z","timestamp":1773317872258,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":13,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819601271","type":"print"},{"value":"9789819601288","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-0128-8_18","type":"book-chapter","created":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T18:18:34Z","timestamp":1731781114000},"page":"207-215","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Fast Solution to\u00a0the\u00a0Fair Ranking Problem Using the\u00a0Sinkhorn Algorithm"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8940-8461","authenticated-orcid":false,"given":"Yuki","family":"Uehara","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4283-0819","authenticated-orcid":false,"given":"Shunnosuke","family":"Ikeda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6906-4323","authenticated-orcid":false,"given":"Naoki","family":"Nishimura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3356-3613","authenticated-orcid":false,"given":"Koya","family":"Ohashi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3765-0755","authenticated-orcid":false,"given":"Yilin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8788-2541","authenticated-orcid":false,"given":"Jie","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1557-8131","authenticated-orcid":false,"given":"Deddy","family":"Jobson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingxia","family":"Zha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takeshi","family":"Matsumoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3560-0036","authenticated-orcid":false,"given":"Noriyoshi","family":"Sukegawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8919-1282","authenticated-orcid":false,"given":"Yuichi","family":"Takano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,12]]},"reference":[{"key":"18_CR1","unstructured":"Altschuler, J., Niles-Weed, J., Rigollet, P.: Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"18_CR2","unstructured":"ApS, M.: The MOSEK optimizer API for Python manual, version 10.2 (2024). https:\/\/docs.mosek.com\/latest\/pythonapi\/index.html"},{"key":"18_CR3","unstructured":"Bhatia, K., et al.: The extreme classification repository: multi-label datasets and code (2016). http:\/\/manikvarma.org\/downloads\/XC\/XMLRepository.html"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Biega, A.J., Gummadi, K.P., Weikum, G.: Equity of attention: amortizing individual fairness in rankings. In: The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 405\u2013414 (2018)","DOI":"10.1145\/3209978.3210063"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Craswell, N., Zoeter, O., Taylor, M., Ramsey, B.: An experimental comparison of click position-bias models. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 87\u201394 (2008)","DOI":"10.1145\/1341531.1341545"},{"key":"18_CR6","unstructured":"Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. Adv. Neural Inf. Process. Syst. 26 (2013)"},{"key":"18_CR7","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations, San Diego (2015)"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Luenberger, D.G., Ye, Y.: Linear and Nonlinear Programming. Springer (2015)","DOI":"10.1007\/978-3-319-18842-3"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Patro, G.K., Biswas, A., Ganguly, N., Gummadi, K.P., Chakraborty, A.: FairRec: two-sided fairness for personalized recommendations in two-sided platforms. In: Proceedings of the Web Conference 2020, pp. 1194\u20131204 (2020)","DOI":"10.1145\/3366423.3380196"},{"key":"18_CR10","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1007\/s00778-021-00697-y","volume":"31","author":"E Pitoura","year":"2022","unstructured":"Pitoura, E., Stefanidis, K., Koutrika, G.: Fairness in rankings and recommendations: an overview. VLDB J. 31, 431\u2013458 (2022)","journal-title":"VLDB J."},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Saito, Y., Joachims, T.: Fair ranking as fair division: impact-based individual fairness in ranking. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1514\u20131524 (2022)","DOI":"10.1145\/3534678.3539353"},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Singh, A., Joachims, T.: Fairness of exposure in rankings. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2219\u20132228 (2018)","DOI":"10.1145\/3219819.3220088"},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Togashi, R., Abe, K., Saito, Y.: Scalable and provably fair exposure control for large-scale recommender systems. In: Proceedings of the ACM Web Conference 2024, pp. 3307\u20133318 (2024)","DOI":"10.1145\/3589334.3645390"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2024: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0128-8_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T19:24:16Z","timestamp":1731785056000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0128-8_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,12]]},"ISBN":["9789819601271","9789819601288"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0128-8_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,12]]},"assertion":[{"value":"12 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}