{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:14:01Z","timestamp":1780766041818,"version":"3.54.1"},"reference-count":20,"publisher":"Association for Computing Machinery (ACM)","issue":"Autumn","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGWEB Newsl."],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:p>Masoud Mansoury is a postdoctoral researcher at Amsterdam Machine Learning Lab at University of Amsterdam, Netherlands. He is also a member of Discovery Lab collaborating with Data Science team at Elsevier Company in the area of recommender systems. Masoud received his PhD in Computer and Information Science from Eindhoven University of Technology, Netherlands, in 2021. He has published his research works in top conferences such as FAccT, RecSys, and CIKM. His research interests include recommender systems, algorithmic bias, and contextual bandits.<\/jats:p>\n          <jats:p>This research conducted by Masoud Mansoury investigated the impact of unfair recommendations on different actors in the system and proposed solutions to tackle the unfairness of recommendations. The solutions were a rating transformation technique that works as a pre-processing step before recommendation generation and a general graph-based solution that works as a post-processing approach after recommendation generation for mitigating the multi-sided exposure bias in the recommendation results. For evaluation, he introduced several metrics for measuring the exposure fairness for items and suppliers, and showed that the proposed metrics better capture the fairness properties in the recommendation results. Extensive experiments on different publicly-available datasets confirmed the superiority of the proposed solutions in improving the exposure fairness for items and suppliers.<\/jats:p>","DOI":"10.1145\/3566100.3566103","type":"journal-article","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T18:21:07Z","timestamp":1669227667000},"page":"1-4","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Understanding and mitigating multi-sided exposure bias in recommender systems"],"prefix":"10.1145","volume":"2022","author":[{"given":"Masoud","family":"Mansoury","sequence":"first","affiliation":[{"name":"University of Amsterdam"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,11,23]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Abdollahpouri H. Adomavicius G. Burke R. Guy I. Jannach D. Kamishima T. Krasnodebski J. and Pizzato L. A. 2019. Beyond personalization: Research directions in multi-stakeholder recommendation. CoRR abs\/1905.01986.  Abdollahpouri H. Adomavicius G. Burke R. Guy I. Jannach D. Kamishima T. Krasnodebski J. and Pizzato L. A. 2019. Beyond personalization: Research directions in multi-stakeholder recommendation. CoRR abs\/1905.01986."},{"key":"e_1_2_1_2_1","volume-title":"KDD workshop on Industrial Recommendation Systems.","author":"Abdollahpouri H.","unstructured":"Abdollahpouri , H. and Mansoury , M . 2020. Multi-sided exposure bias in recommendation . KDD workshop on Industrial Recommendation Systems. Abdollahpouri, H. and Mansoury, M. 2020. Multi-sided exposure bias in recommendation. KDD workshop on Industrial Recommendation Systems."},{"key":"e_1_2_1_3_1","unstructured":"Burke R. 2017. Multisided fairness for recommendation. CoRR abs\/1707.00093.  Burke R. 2017. Multisided fairness for recommendation. CoRR abs\/1707.00093."},{"key":"e_1_2_1_4_1","unstructured":"Chen J. Dong H. Wang X. Feng F. Wang M. and He X. 2020. Bias and debias in recommender system: A survey and future directions. arXiv preprint arXiv:2010.03240.  Chen J. Dong H. Wang X. Feng F. Wang M. and He X. 2020. Bias and debias in recommender system: A survey and future directions. arXiv preprint arXiv:2010.03240."},{"key":"e_1_2_1_5_1","volume-title":"In Conference on Fairness, Accountability and Transparency. 172--186","author":"Ekstrand M. D.","unstructured":"Ekstrand , M. D. , Tian , M. , Azpiazu , I. M. , Ekstrand , J. D. , Anuyah , O. , McNeill , D. , and Pera , M. S . 2018. All the cool kids, how do they fit in?: Popularity and demographic biases in recommender evaluation and effectiveness . In In Conference on Fairness, Accountability and Transparency. 172--186 . Ekstrand, M. D., Tian, M., Azpiazu, I. M., Ekstrand, J. D., Anuyah, O., McNeill, D., and Pera, M. S. 2018. All the cool kids, how do they fit in?: Popularity and demographic biases in recommender evaluation and effectiveness. In In Conference on Fairness, Accountability and Transparency. 172--186."},{"key":"e_1_2_1_6_1","volume-title":"International conference on user modeling, adaptation, and personalization. Springer","author":"Jannach D.","unstructured":"Jannach , D. , Lerche , L. , Gedikli , F. , and Bonnin , G . 2013. What recommenders recommendan analysis of accuracy, popularity, and sales diversity effects . In International conference on user modeling, adaptation, and personalization. Springer , Berlin, Heidelberg, 25--37. Jannach, D., Lerche, L., Gedikli, F., and Bonnin, G. 2013. What recommenders recommendan analysis of accuracy, popularity, and sales diversity effects. In International conference on user modeling, adaptation, and personalization. Springer, Berlin, Heidelberg, 25--37."},{"key":"e_1_2_1_7_1","volume-title":"In 11th International Conference on Data Mining Workshops. 643--650","author":"Kamishima T.","unstructured":"Kamishima , T. , Akaho , S. , and Sakuma , J . 2011. Fairness-aware learning through regularization approach . In In 11th International Conference on Data Mining Workshops. 643--650 . Kamishima, T., Akaho, S., and Sakuma, J. 2011. Fairness-aware learning through regularization approach. In In 11th International Conference on Data Mining Workshops. 643--650."},{"key":"e_1_2_1_8_1","unstructured":"Mansoury M. Abdollahpouri H. Mobasher B. Pechenizkiy M. Burke R. and Sabouri M. 2021. Unbiased cascade bandits: Mitigating exposure bias in online learning to rank recommendation. arXiv preprint arXiv:2108.03440.  Mansoury M. Abdollahpouri H. Mobasher B. Pechenizkiy M. Burke R. and Sabouri M. 2021. Unbiased cascade bandits: Mitigating exposure bias in online learning to rank recommendation. arXiv preprint arXiv:2108.03440."},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the 28th ACM conference on user modeling, adaptation and personalization. 154--162","author":"Mansoury M.","unstructured":"Mansoury , M. , Abdollahpouri , H. , Pechenizkiy , M. , Mobasher , B. , and Burke , R . 2020a. Fair-match: A graph-based approach for improving aggregate diversity in recommender systems . In Proceedings of the 28th ACM conference on user modeling, adaptation and personalization. 154--162 . Mansoury, M., Abdollahpouri, H., Pechenizkiy, M., Mobasher, B., and Burke, R. 2020a. Fair-match: A graph-based approach for improving aggregate diversity in recommender systems. In Proceedings of the 28th ACM conference on user modeling, adaptation and personalization. 154--162."},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2145--2148","author":"Mansoury M.","unstructured":"Mansoury , M. , Abdollahpouri , H. , Pechenizkiy , M. , Mobasher , B. , and Burke , R . 2020b. Feedback loop and bias amplification in recommender systems . In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2145--2148 . Mansoury, M., Abdollahpouri, H., Pechenizkiy, M., Mobasher, B., and Burke, R. 2020b. Feedback loop and bias amplification in recommender systems. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2145--2148."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3470948"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437910"},{"key":"e_1_2_1_13_1","volume-title":"RMSE Workshop at RecSys'19","author":"Mansoury M.","unstructured":"Mansoury , M. , Mobasher , B. , Burke , R. , and Pechenizkiy , M . 2019. Bias disparity in collaborative recommendation: Algorithmic evaluation and comparison . RMSE Workshop at RecSys'19 . Mansoury, M., Mobasher, B., Burke, R., and Pechenizkiy, M. 2019. Bias disparity in collaborative recommendation: Algorithmic evaluation and comparison. 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In In International Conference on Machine Learning. 325--333."}],"container-title":["ACM SIGWEB Newsletter"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3566100.3566103","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3566100.3566103","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:31Z","timestamp":1750182691000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3566100.3566103"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9]]},"references-count":20,"journal-issue":{"issue":"Autumn","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["10.1145\/3566100.3566103"],"URL":"https:\/\/doi.org\/10.1145\/3566100.3566103","relation":{},"ISSN":["1931-1745","1931-1435"],"issn-type":[{"value":"1931-1745","type":"print"},{"value":"1931-1435","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9]]},"assertion":[{"value":"2022-11-23","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}