{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:19:12Z","timestamp":1743041952064,"version":"3.40.3"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031560682"},{"type":"electronic","value":"9783031560699"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-56069-9_7","type":"book-chapter","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T08:17:45Z","timestamp":1711095465000},"page":"90-105","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Optimizing Ranking in\u00a0Grid-Layout for\u00a0Provider-Side Fairness"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8874-6645","authenticated-orcid":false,"given":"Amifa","family":"Raj","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2467-0108","authenticated-orcid":false,"given":"Michael D.","family":"Ekstrand","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,23]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57","author":"Y Benjamini","year":"1995","unstructured":"Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc.: Ser. B (Methodol.) 57, 289\u2013300 (1995)","journal-title":"J. Roy. Stat. Soc.: Ser. B (Methodol.)"},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 230\u2013237. Association for Computing Machinery, New York (1999). isbn: 1581130961","DOI":"10.1145\/312624.312682"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189\u20131232 (2001)","DOI":"10.1214\/aos\/1013203451"},{"key":"7_CR4","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1145\/963770.963776","volume":"22","author":"M Deshpande","year":"2004","unstructured":"Deshpande, M., Karypis, G.: Item-based Top-n recommendation algorithms. ACM Trans. Inf. Syst. (TOIS) 22, 143\u2013177 (2004)","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Burges, C., et al.: Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 89\u201396 (2005)","DOI":"10.1145\/1102351.1102363"},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th International Conference on Machine Learning, pp. 129\u2013136 (2007)","DOI":"10.1145\/1273496.1273513"},{"key":"7_CR7","unstructured":"Li, P., Wu, Q., Burges, C.: Mcrank: learning to rank using multiple classification and gradient boosting. Adv. Neural Inf. Process. Syst. 20 (2007)"},{"key":"7_CR8","unstructured":"Shrestha, S., Lenz, K.: Eye gaze patterns while searching vs. browsing a website. Usability News 9, 1\u20139 (2007)"},{"key":"7_CR9","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1167\/7.14.4","volume":"7","author":"BW Tatler","year":"2007","unstructured":"Tatler, B.W.: The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions. J. Vis. 7, 4\u20134 (2007)","journal-title":"J. Vis."},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Xu, J., Li, H.: Adarank: a boosting algorithm for information retrieval. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 391\u2013398 (2007)","DOI":"10.1145\/1277741.1277809"},{"key":"7_CR11","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":"7_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1416950.1416952","volume":"27","author":"A Moffat","year":"2008","unstructured":"Moffat, A., Zobel, J.: Rank-biased precision for measurement of retrieval effectiveness. ACM Trans. Inf. Syst. (TOIS) 27, 1\u201327 (2008)","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Taylor, M., Guiver, J., Robertson, S., Minka, T.: Softrank: optimizing nonsmooth rank metrics. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 77\u201386 (2008)","DOI":"10.1145\/1341531.1341544"},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Xia, F., Liu, T.-Y., Wang, J., Zhang, W., Li, H.: Listwise approach to learning to rank: theory and algorithm. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1192\u20131199 (2008)","DOI":"10.1145\/1390156.1390306"},{"key":"7_CR15","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452\u2013461. AUAI Press, Montreal, Quebec, Canada (2009), isbn: 9780974903958"},{"key":"7_CR16","first-page":"81","volume":"11","author":"CJ Burges","year":"2010","unstructured":"Burges, C.J.: From ranknet to lambdarank to lambdamart: an overview. Learning 11, 81 (2010)","journal-title":"Learning"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Djamasbi, S., Siegel, M., Tullis, T.: Visual hierarchy and viewing behavior: an eye tracking study. In: International Conference on Human-Computer Interaction, pp. 331\u2013340 (2011)","DOI":"10.1007\/978-3-642-21602-2_36"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Tak\u00e1cs, G., Pil\u00e1szy, I., Tikk, D.: Applications of the conjugate gradient method for implicit feedback collaborative filtering. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 297\u2013300. Association for Computing Machinery, Chicago (2011). isbn: 9781450306836","DOI":"10.1145\/2043932.2043987"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 214\u2013226 (2012)","DOI":"10.1145\/2090236.2090255"},{"key":"7_CR20","unstructured":"Hsu, H., Lachenbruch, P.A.: Paired t test. Wiley StatsRef: statistics reference online (2014)"},{"key":"7_CR21","unstructured":"Lan, Y., Zhu, Y., Guo, J., Niu, S., Cheng, X.: Position-aware ListMLE: a sequential learning process for ranking. In: UAI, pp. 449\u2013458 (2014)"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Chang, S., Harper, F.M., Konstan, J.A.: Gaze prediction for recommender systems. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 131\u2013138 (2016)","DOI":"10.1145\/2959100.2959150"},{"key":"7_CR23","doi-asserted-by":"crossref","unstructured":"Xie, X., et al. Investigating examination behavior of image search users. In Proceedings of the 40th International ACM Sigir Conference on Research and Development in Information Retrieval, pp. 275\u2013284 (2017)","DOI":"10.1145\/3077136.3080799"},{"key":"7_CR24","doi-asserted-by":"crossref","unstructured":"Yang, K., Stoyanovich, J.: Measuring fairness in ranked outputs. In: Proceedings of the 29th International Conference on Scientific and Statistical Database Management, pp. 1\u20136 (2017)","DOI":"10.1145\/3085504.3085526"},{"key":"7_CR25","doi-asserted-by":"publisher","unstructured":"Zehlike, M., et al.: FA*IR: a fair top-k ranking algorithm. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 1569\u20131578. Association for Computing Machinery, Singapore (2017). isbn: 9781450349185. https:\/\/doi.org\/10.1145\/3132847.3132938","DOI":"10.1145\/3132847.3132938"},{"key":"7_CR26","doi-asserted-by":"crossref","unstructured":"Biega, A. J., Gummadi, K.P., Weikum, G.: Equity of attention: amortizing individual fairness in rankings. In: Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 405\u2013414 (2018)","DOI":"10.1145\/3209978.3210063"},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Ekstrand, M.D., Tian, M., Kazi, M.R.I., Mehrpouyan, H., Kluver, D.: Exploring author gender in book rating and recommendation. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 242\u2013250 (2018)","DOI":"10.1145\/3240323.3240373"},{"key":"7_CR28","doi-asserted-by":"publisher","unstructured":"Singh, A., Joachims, T.: Fairness of exposure in rankings. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2219\u20132228. Association for Computing Machinery, London (2018). isbn: 9781450355520. https:\/\/doi.org\/10.1145\/3219819.3220088","DOI":"10.1145\/3219819.3220088"},{"key":"7_CR29","doi-asserted-by":"publisher","unstructured":"Wan, M., McAuley, J.: Item recommendation on monotonic behavior chains. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 86\u201394. Association for Computing Machinery, Vancouver (2018). isbn: 9781450359016. https:\/\/doi.org\/10.1145\/3240323.3240369","DOI":"10.1145\/3240323.3240369"},{"key":"7_CR30","unstructured":"Wu, L., Hsieh, C.-J., Sharpnack, J.: SQL-RANK: a listwise approach to collaborative ranking. In: International Conference on Machine Learning, pp. 5315\u20135324 (2018)"},{"key":"7_CR31","doi-asserted-by":"crossref","unstructured":"Beutel, A., et al.: Fairness in recommendation ranking through pairwise comparisons. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2212\u20132220 (2019)","DOI":"10.1145\/3292500.3330745"},{"key":"7_CR32","unstructured":"Biega, A.J., Diaz, F., Ekstrand, M.D., Kohlmeier, S.: Overview of the TREC 2019 fair ranking track. In: The Twenty-Eighth Text REtrieval Conference (TREC 2019) Proceedings (2019)"},{"key":"7_CR33","doi-asserted-by":"crossref","unstructured":"Geyik, S.C., Ambler, S., Kenthapadi, K.: Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In: Proceedings of the 25th ACM Sigkdd International Conference on Knowledge Discovery & Data Mining, pp. 2221\u20132231 (2019)","DOI":"10.1145\/3292500.3330691"},{"key":"7_CR34","doi-asserted-by":"crossref","unstructured":"Liu, W., Guo, J., Sonboli, N., Burke, R., Zhang, S.: Personalized fairnessaware re-ranking for microlending. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 467\u2013471 (2019)","DOI":"10.1145\/3298689.3347016"},{"key":"7_CR35","doi-asserted-by":"crossref","unstructured":"Pei, C., et al.: Personalized re-ranking for recommendation. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 3\u201311 (2019)","DOI":"10.1145\/3298689.3347000"},{"key":"7_CR36","doi-asserted-by":"publisher","unstructured":"Sapiezynski, P., Zeng, W., E Robertson, R., Mislove, A., Wilson, C.: Quantifying the impact of user attention on fair group representation in ranked lists. In: Companion Proceedings of The 2019 World Wide Web Conference, pp. 553\u2013562. Association for Computing Machinery, San Francisco (2019). isbn: 9781450366755. https:\/\/doi.org\/10.1145\/3308560.3317595","DOI":"10.1145\/3308560.3317595"},{"key":"7_CR37","doi-asserted-by":"crossref","unstructured":"Xie, X., et al.: Grid-based evaluation metrics for web image search. In: The World Wide Web Conference, pp. 2103\u20132114 (2019)","DOI":"10.1145\/3308558.3313514"},{"key":"7_CR38","doi-asserted-by":"publisher","unstructured":"Diaz, F., Mitra, B., Ekstrand, M.D., Biega, A.J., Carterette, B.: Evaluating stochastic rankings with expected exposure. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 275\u2013284. Association for Computing Machinery, Virtual Event (2020). isbn: 9781450368599. https:\/\/doi.org\/10.1145\/3340531.3411962","DOI":"10.1145\/3340531.3411962"},{"key":"7_CR39","doi-asserted-by":"publisher","unstructured":"Ekstrand, M.D.: LensKit for python: next-generation software for recommender systems experiments. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2999\u20133006. Association for Computing Machinery, Virtual Event (2020). isbn: 9781450368599. https:\/\/doi.org\/10.1145\/3340531.3412778","DOI":"10.1145\/3340531.3412778"},{"key":"7_CR40","doi-asserted-by":"crossref","unstructured":"Ekstrand, M.D., Kluver, D.: Exploring author gender in book rating and recommendation. User Model. User-Adapt. Interact. (2020). https:\/\/md.ekstrandom.net\/pubs\/bag-extended","DOI":"10.1007\/s11257-020-09284-2"},{"key":"7_CR41","unstructured":"Jiang, H., Nachum, O.: Identifying and correcting label bias in machine learning. In: International Conference on Artificial Intelligence and Statistics, pp. 702\u2013712 (2020)"},{"key":"7_CR42","doi-asserted-by":"crossref","unstructured":"Narasimhan, H., Cotter, A., Gupta, M.R., Wang, S.: Pairwise fairness for ranking and regression. In: AAA, vol. I, pp. 5248\u20135255 (2020)","DOI":"10.1609\/aaai.v34i04.5970"},{"key":"7_CR43","unstructured":"Raj, A., Wood, C., Montoly, A., Ekstrand, M.D.: Comparing fair ranking metrics (2020). arXiv preprint arXiv:2009.01311"},{"key":"7_CR44","doi-asserted-by":"crossref","unstructured":"Pitoura, E., Stefanidis, K., Koutrika, G.: Fairness in rankings and recommendations: an overview. VLDB J. 1\u201328 (2021)","DOI":"10.1109\/MDM52706.2021.00013"},{"key":"7_CR45","unstructured":"Sonoda, R.: A Pre-processing Method for Fairness in Ranking. arXiv preprint arXiv:2110.15503 (2021)"},{"key":"7_CR46","doi-asserted-by":"crossref","unstructured":"Chen, S., et al.: Reinforcement Re-ranking with 2D Grid-based Recommendation Panels. arXiv preprint arXiv:2204.04954 (2022)","DOI":"10.1145\/3624918.3625311"},{"key":"7_CR47","doi-asserted-by":"crossref","unstructured":"Ekstrand, M.D., Das, A., Burke, R., Diaz, F., et al.: Fairness in information access systems. Found. Trends\u00ae Inf. Retr. 16, 1\u2013177 (2022)","DOI":"10.1561\/1500000079"},{"key":"7_CR48","doi-asserted-by":"crossref","unstructured":"Raj, A., Ekstrand, M.D.: Measuring fairness in ranked results: an analytical and empirical comparison. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 726\u2013736 (2022)","DOI":"10.1145\/3477495.3532018"},{"key":"7_CR49","doi-asserted-by":"crossref","unstructured":"Ekstrand, M.D., McDonald, G., Raj, A., Johnson, I.: Overview of the TREC 2022 Fair Ranking Track. arXiv preprint arXiv:2302.05558 (2023)","DOI":"10.6028\/NIST.SP.500-338.fair-overview"},{"key":"7_CR50","doi-asserted-by":"crossref","unstructured":"Pinney, C., Raj, A., Hanna, A., Ekstrand, M.D.: Much ado about gender: current practices and future recommendations for appropriate gender-aware information access. arXiv preprint arXiv:2301.04780 (2023)","DOI":"10.1145\/3576840.3578316"},{"key":"7_CR51","unstructured":"Raj, A., Ekstrand, M.: Unified browsing models for linear and grid layouts. arXiv preprint arXiv:2310.12524 (2023)"},{"key":"7_CR52","unstructured":"Raj, A., Ekstrand, M.D.: Towards measuring fairness in grid layout in recommender systems. arXiv preprint arXiv:2309.10271 (2023)"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-56069-9_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T21:26:12Z","timestamp":1731619572000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-56069-9_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031560682","9783031560699"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-56069-9_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"23 March 2024","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"24 March 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 March 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ecir2024.org\/","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":"578","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":"110","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":"69","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":"4","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)"}},{"value":"31 (Tracks: Workshop, Tutorial, Industry, Doctoral Consortium)","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}