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A specific form of fairness is supplier exposure fairness, where the objective is to ensure equitable coverage of items across all suppliers in recommendations provided to users. This is especially important in multistakeholder recommendation scenarios where it may be important to optimize utilities not just for the end user but also for other stakeholders such as item sellers or producers who desire a fair representation of their items. This type of supplier fairness is sometimes accomplished by attempting to increase aggregate diversity to mitigate popularity bias and to improve the coverage of long-tail items in recommendations. In this article, we introduce FairMatch, a general graph-based algorithm that works as a post-processing approach after recommendation generation to improve exposure fairness for items and suppliers. The algorithm iteratively adds high-quality items that have low visibility or items from suppliers with low exposure to the users\u2019 final recommendation lists. A comprehensive set of experiments on two datasets and comparison with state-of-the-art baselines show that FairMatch, although it significantly improves exposure fairness and aggregate diversity, maintains an acceptable level of relevance of the recommendations.<\/jats:p>","DOI":"10.1145\/3470948","type":"journal-article","created":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T22:04:26Z","timestamp":1637100266000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":42,"title":["A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems"],"prefix":"10.1145","volume":"40","author":[{"given":"Masoud","family":"Mansoury","sequence":"first","affiliation":[{"name":"Eindhoven University of Technology, MB Eindhoven, The Netherlands"}]},{"given":"Himan","family":"Abdollahpouri","sequence":"additional","affiliation":[{"name":"Northwestern University, Evanston, IL, USA"}]},{"given":"Mykola","family":"Pechenizkiy","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology, MB Eindhoven, The Netherlands"}]},{"given":"Bamshad","family":"Mobasher","sequence":"additional","affiliation":[{"name":"DePaul University, South Wabash, Chicago, IL, USA"}]},{"given":"Robin","family":"Burke","sequence":"additional","affiliation":[{"name":"University of Colorado Boulder, Boulder, CO, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,11,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-005-6468-9"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109912"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 32nd International Flairs Conference.","author":"Abdollahpouri Himan","year":"2019","unstructured":"Himan Abdollahpouri , Robin Burke , and Bamshad Mobasher . 2019 . Managing popularity bias in recommender systems with personalized re-ranking . In Proceedings of the 32nd International Flairs Conference. Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2019. Managing popularity bias in recommender systems with personalized re-ranking. In Proceedings of the 32nd International Flairs Conference."},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the KDD Workshop on Industrial Recommendation Systems.","author":"Abdollahpouri Himan","year":"2020","unstructured":"Himan Abdollahpouri and Masoud Mansoury . 2020 . Multi-sided exposure bias in recommendation . In Proceedings of the KDD Workshop on Industrial Recommendation Systems. Himan Abdollahpouri and Masoud Mansoury. 2020. Multi-sided exposure bias in recommendation. In Proceedings of the KDD Workshop on Industrial Recommendation Systems."},{"key":"e_1_2_1_5_1","unstructured":"Himan Abdollahpouri Masoud Mansoury Robin Burke and Bamshad Mobasher. 2020. 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