{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T22:20:38Z","timestamp":1772749238855,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031372483","type":"print"},{"value":"9783031372490","type":"electronic"}],"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\/978-3-031-37249-0_1","type":"book-chapter","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T04:02:11Z","timestamp":1689307331000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Study on\u00a0Accuracy, Miscalibration, and\u00a0Popularity Bias in\u00a0Recommendations"],"prefix":"10.1007","author":[{"given":"Dominik","family":"Kowald","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregor","family":"Mayr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Markus","family":"Schedl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elisabeth","family":"Lex","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,15]]},"reference":[{"key":"1_CR1","unstructured":"Abdollahpouri, H., Burke, R., Mobasher, B.: Managing popularity bias in recommender systems with personalized re-ranking. In: The Thirty-second International Flairs Conference (2019)"},{"key":"1_CR2","unstructured":"Abdollahpouri, H., Mansoury, M., Burke, R., Mobasher, B.: The impact of popularity bias on fairness and calibration in recommendation. arXiv preprint arXiv:1910.05755 (2019)"},{"key":"1_CR3","unstructured":"Abdollahpouri, H., Mansoury, M., Burke, R., Mobasher, B.: The unfairness of popularity Bias in recommendation. arXiv preprint arXiv:1907.13286 (2019)"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Abdollahpouri, H., Mansoury, M., Burke, R., Mobasher, B.: The connection between popularity bias, calibration, and fairness in recommendation. In: Fourteenth ACM Conference on Recommender Systems, pp. 726\u2013731 (2020)","DOI":"10.1145\/3383313.3418487"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Abdollahpouri, H., Mansoury, M., Burke, R., Mobasher, B., Malthouse, E.: User-centered evaluation of popularity bias in recommender systems. In: Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, pp. 119\u2013129 (2021)","DOI":"10.1145\/3450613.3456821"},{"issue":"5","key":"1_CR6","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1109\/TKDE.2011.15","volume":"24","author":"G Adomavicius","year":"2011","unstructured":"Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 24(5), 896\u2013911 (2011)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Baeza-Yates, R.: Bias in search and recommender systems. In: Fourteenth ACM Conference on Recommender Systems, p. 2 (2020)","DOI":"10.1145\/3383313.3418435"},{"key":"1_CR8","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1007\/978-1-4899-7637-6_26","volume-title":"Recommender Systems Handbook","author":"P Castells","year":"2015","unstructured":"Castells, P., Hurley, N.J., Vargas, S.: Novelty and diversity in recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 881\u2013918. Springer, Boston, MA (2015). https:\/\/doi.org\/10.1007\/978-1-4899-7637-6_26"},{"key":"1_CR9","unstructured":"Ekstrand, M.D., et al.: All the cool kids, how do they fit in?: Popularity and demographic biases in recommender evaluation and effectiveness. In: Conference on Fairness, Accountability and Transparency, pp. 172\u2013186. PMLR (2018)"},{"key":"1_CR10","unstructured":"George, T., Merugu, S.: A scalable collaborative filtering framework based on co-clustering. In: Fifth IEEE International Conference on Data Mining (ICDM2005), p. 4. IEEE (2005)"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Harper, F.M., Konstan, J.A.: The MovieLens datasets: History and context. ACM Trans. Interact. Intell. Syst. 5, 1\u201319 (2015)","DOI":"10.1145\/2827872"},{"issue":"52","key":"1_CR12","doi-asserted-by":"publisher","first-page":"2174","DOI":"10.21105\/joss.02174","volume":"5","author":"N Hug","year":"2020","unstructured":"Hug, N.: Surprise: a python library for recommender systems. J. Open Source Soft. 5(52), 2174 (2020)","journal-title":"J. Open Source Soft."},{"issue":"4","key":"1_CR13","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1145\/582415.582418","volume":"20","author":"K J\u00e4rvelin","year":"2002","unstructured":"J\u00e4rvelin, K., Kek\u00e4l\u00e4inen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. (TOIS) 20(4), 422\u2013446 (2002)","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"issue":"1","key":"1_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1644873.1644874","volume":"4","author":"Y Koren","year":"2010","unstructured":"Koren, Y.: Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans. Knowl. Discov. Data (TKDD) 4(1), 1\u201324 (2010)","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"issue":"2","key":"1_CR15","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1109\/TKDE.2018.2832132","volume":"31","author":"D Kotzias","year":"2018","unstructured":"Kotzias, D., Lichman, M., Smyth, P.: Predicting consumption patterns with repeated and novel events. IEEE Trans. Knowl. Data Eng. 31(2), 371\u2013384 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1_CR16","unstructured":"Kowald, D., Dennerlein, S., Theiler, D., Walk, S., Trattner, C.: The social semantic server: a framework to provide services on social semantic network data. In: 9th International Conference on Semantic Systems, I-SEMANTICS 2013, pp. 50\u201354. CEUR (2013)"},{"key":"1_CR17","doi-asserted-by":"publisher","unstructured":"Kowald, D., Lacic, E.: Popularity Bias in collaborative filtering-based multimedia recommender systems. In: Boratto, L., Faralli, S., Marras, M., Stilo, G. (eds.) Advances in Bias and Fairness in Information Retrieval. BIAS 2022. Communications in Computer and Information Science, vol. 1610, pp. 1\u201311. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-09316-6_1","DOI":"10.1007\/978-3-031-09316-6_1"},{"issue":"1","key":"1_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1140\/epjds\/s13688-021-00268-9","volume":"10","author":"D Kowald","year":"2021","unstructured":"Kowald, D., Muellner, P., Zangerle, E., Bauer, C., Schedl, M., Lex, E.: Support the underground: characteristics of beyond-mainstream music listeners. EPJ Data Sci. 10(1), 1\u201326 (2021). https:\/\/doi.org\/10.1140\/epjds\/s13688-021-00268-9","journal-title":"EPJ Data Sci."},{"key":"1_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/978-3-030-45442-5_5","volume-title":"Advances in Information Retrieval","author":"D Kowald","year":"2020","unstructured":"Kowald, D., Schedl, M., Lex, E.: The unfairness of popularity bias in music recommendation: a reproducibility study. In: Jose, J.M., et al. (eds.) ECIR 2020. LNCS, vol. 12036, pp. 35\u201342. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-45442-5_5"},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Lacic, E., Kowald, D., Parra, D., Kahr, M., Trattner, C.: Towards a scalable social recommender engine for online marketplaces: the case of apache solr. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 817\u2013822 (2014)","DOI":"10.1145\/2567948.2579245"},{"key":"1_CR21","unstructured":"Lacic, E., Kowald, D., Traub, M., Luzhnica, G., Simon, J.P., Lex, E.: Tackling cold-start users in recommender systems with indoor positioning systems. In: Poster Proceedings of the 9th $$\\{$$ACM$$\\}$$ Conference on Recommender Systems. ACM (2015)"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Lesota, O., et al.: Analyzing item popularity bias of music recommender systems: are different genders equally affected? In: Proceedings of the 15th ACM Conference on Recommender Systems, pp. 601\u2013606 (2021)","DOI":"10.1145\/3460231.3478843"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Lex, E., Kowald, D., Seitlinger, P., Tran, T.N.T., Felfernig, A., Schedl, M., et al.: Psychology-informed recommender systems. Found. Trends\u00ae Inf. Retrieval 15(2), 134\u2013242 (2021)","DOI":"10.1561\/1500000090"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Lin, K., Sonboli, N., Mobasher, B., Burke, R.: Calibration in collaborative filtering recommender systems: a user-centered analysis. In: Proceedings of the 31st ACM Conference on Hypertext and Social Media, pp. 197\u2013206. HT 2020, Association for Computing Machinery, New York, NY, USA (2020)","DOI":"10.1145\/3372923.3404793"},{"issue":"2","key":"1_CR25","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1109\/TII.2014.2308433","volume":"10","author":"X Luo","year":"2014","unstructured":"Luo, X., Zhou, M., Xia, Y., Zhu, Q.: An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Trans. Industr. Inf. 10(2), 1273\u20131284 (2014)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"1_CR26","unstructured":"Pacula, M.: A matrix factorization algorithm for music recommendation using implicit user feedback. Maciej Pacula (2009)"},{"issue":"4","key":"1_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3190616","volume":"51","author":"M Quadrana","year":"2018","unstructured":"Quadrana, M., Cremonesi, P., Jannach, D.: Sequence-aware recommender systems. ACM Comput. Surv. (CSUR) 51(4), 1\u201336 (2018)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Schedl, M.: The LFM-1b dataset for music retrieval and recommendation. In: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, pp. 103\u2013110 (2016)","DOI":"10.1145\/2911996.2912004"},{"key":"1_CR29","doi-asserted-by":"crossref","unstructured":"Steck, H.: Calibrated recommendations. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 154\u2013162. RecSys 2018, Association for Computing Machinery, New York, NY, USA (2018)","DOI":"10.1145\/3240323.3240372"},{"issue":"1","key":"1_CR30","doi-asserted-by":"publisher","first-page":"79","DOI":"10.3354\/cr030079","volume":"30","author":"CJ Willmott","year":"2005","unstructured":"Willmott, C.J., Matsuura, K.: Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance. Climate Res. 30(1), 79\u201382 (2005)","journal-title":"Climate Res."}],"container-title":["Communications in Computer and Information Science","Advances in Bias and Fairness in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-37249-0_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T04:02:21Z","timestamp":1689307341000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-37249-0_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031372483","9783031372490"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-37249-0_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BIAS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Algorithmic Bias in Search and Recommendation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dublin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","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":"2 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ibpria2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/biasinrecsys.github.io\/ecir2023\/","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36","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":"10","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":"4","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":"28% - 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":"1","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)"}}]}}