Elizabeth Gómez
Ludovico Boratto
Mirko Marras
ABSTRACT
Educational recommender systems channel most of the research efforts on the effectiveness of the recommended items. While teachers have a central role in online platforms, the impact of recommender systems for teachers in terms of the exposure such systems give to the courses is an under-explored area. In this paper, we consider data coming from a real-world platform and analyze the distribution of the recommendations w.r.t. the geographical provenience of the teachers. We observe that data is highly imbalanced towards the United States, in terms of offered courses and of interactions. These imbalances are exacerbated by recommender systems, which overexpose the country w.r.t. its representation in the data, thus generating unfairness for teachers outside that country. To introduce equity, we propose an approach that regulates the share of recommendations given to the items produced in a country (visibility) and the position of the items in the recommended list (exposure).
Supplemental Material
- Alejandro Bellog'i n, Pablo Castells, and Ivá n Cantador. 2017. Statistical biases in Information Retrieval metrics for recommender systems. Inf. Retr. Journal, Vol. 20, 6 (2017), 606--634.Google Scholar
- Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, and Cristos Goodrow. 2019. Fairness in Recommendation Ranking through Pairwise Comparisons. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019. ACM, 2212--2220. https://doi.org/10.1145/3292500.3330745Google ScholarDigital Library
- Asia J. Biega, Krishna P. Gummadi, and Gerhard Weikum. 2018. Equity of Attention: Amortizing Individual Fairness in Rankings. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018. ACM, 405--414. https://doi.org/10.1145/3209978.3210063Google ScholarDigital Library
- Ludovico Boratto, Gianni Fenu, and Mirko Marras. 2019. The Effect of Algorithmic Bias on Recommender Systems for Massive Open Online Courses. In Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14--18, 2019, Proceedings, Part I (Lecture Notes in Computer Science, Vol. 11437), Leif Azzopardi, Benno Stein, Norbert Fuhr, Philipp Mayr, Claudia Hauff, and Djoerd Hiemstra (Eds.). Springer, 457--472. https://doi.org/10.1007/978--3-030--15712--8_30Google ScholarCross Ref
- Robin Burke, Nasim Sonboli, and Aldo Ordonez-Gauger. 2018. Balanced Neighborhoods for Multi-sided Fairness in Recommendation. In Conference on Fairness, Accountability and Transparency, FAT 2018 (Proceedings of Machine Learning Research, Vol. 81). PMLR, 202--214. http://proceedings.mlr.press/v81/burke18a.htmlGoogle Scholar
- Jaime G. Carbonell and Jade Goldstein. 1998. The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries. In SIGIR '98: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 335--336. https://doi.org/10.1145/290941.291025Google ScholarDigital Library
- L. Elisa Celis, Damian Straszak, and Nisheeth K. Vishnoi. 2018. Ranking with Fairness Constraints. In 45th International Colloquium on Automata, Languages, and Programming, ICALP 2018 (LIPIcs, Vol. 107). Schloss Dagstuhl - Leibniz-Zentrum fü r Informatik, 28:1--28:15. https://doi.org/10.4230/LIPIcs.ICALP.2018.28Google ScholarCross Ref
- Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. 2010. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010. ACM, 39--46. https://doi.org/10.1145/1864708.1864721Google ScholarDigital Library
- Danilo Dess`i, Gianni Fenu, Mirko Marras, and Diego Reforgiato Recupero. 2018. COCO: Semantic-Enriched Collection of Online Courses at Scale with Experimental Use Cases. In Trends and Advances in Information Systems and Technologies - Volume 2 [WorldCIST'18, Naples, Italy, March 27--29, 2018] (Advances in Intelligent Systems and Computing, Vol. 746), Á lvaro Rocha, Hojjat Adeli, Lu'i s Paulo Reis, and Sandra Costanzo (Eds.). Springer, 1386--1396. https://doi.org/10.1007/978--3--319--77712--2_133Google ScholarCross Ref
- Francesco Fabbri, Francesco Bonchi, Ludovico Boratto, and Carlos Castillo. 2020. The Effect of Homophily on Disparate Visibility of Minorities in People Recommender Systems. In Proceedings of the Fourteenth International AAAI Conference on Web and Social Media, ICWSM 2020. AAAI Press, 165--175. https://aaai.org/ojs/index.php/ICWSM/article/view/7288Google ScholarCross Ref
- Sara Hajian, Francesco Bonchi, and Carlos Castillo. 2016. Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining. In ACM SIGKDD International Conf. on Knowledge Discovery and Data Mining. ACM, 2125--2126.Google ScholarDigital Library
- Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl. 2002. An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms. Inf. Retr., Vol. 5, 4 (2002), 287--310. https://doi.org/10.1023/A:1020443909834Google ScholarDigital Library
- Kalervo J"a rvelin and Jaana Kek"a l"a inen. 2002. Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst., Vol. 20, 4 (2002), 422--446. https://doi.org/10.1145/582415.582418Google ScholarDigital Library
- Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2018. Recommendation Independence. In Conference on Fairness, Accountability and Transparency, FAT 2018 (Proceedings of Machine Learning Research, Vol. 81). PMLR, 187--201. http://proceedings.mlr.press/v81/kamishima18a.htmlGoogle Scholar
- Yeqin Kang. 2014. An analysis on SPOC: Post-MOOC era of online education. Tsinghua Journal of Education, Vol. 35, 1 (2014), 85--93.Google Scholar
- Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 426--434. https://doi.org/10.1145/1401890.1401944Google ScholarDigital Library
- Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. IEEE Computer, Vol. 42, 8 (2009), 30--37. https://doi.org/10.1109/MC.2009.263Google ScholarDigital Library
- Weiwen Liu, Jun Guo, Nasim Sonboli, Robin Burke, and Shengyu Zhang. 2019. Personalized fairness-aware re-ranking for microlending. In Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, September 16--20, 2019, Toine Bogers, Alan Said, Peter Brusilovsky, and Domonkos Tikk (Eds.). ACM, 467--471. https://doi.org/10.1145/3298689.3347016Google ScholarDigital Library
- Mirko Marras, Ludovico Boratto, Guilherme Ramos, and Gianni Fenu. 2020. Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations. CoRR, Vol. abs/2006.04282 (2020). arxiv: 2006.04282 https://arxiv.org/abs/2006.04282Google Scholar
- Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, and Fernando Diaz. 2018. Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018. ACM, 2243--2251. https://doi.org/10.1145/3269206.3272027Google ScholarDigital Library
- Xia Ning, Christian Desrosiers, and George Karypis. 2015. A Comprehensive Survey of Neighborhood-Based Recommendation Methods. In Recommender Systems Handbook, Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). Springer, 37--76. https://doi.org/10.1007/978--1--4899--7637--6_2Google ScholarCross Ref
- Gourab K. Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, and Abhijnan Chakraborty. 2020. FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms. In WWW '20: The Web Conference 2020. ACM / IW3C2, 1194--1204. https://doi.org/10.1145/3366423.3380196Google ScholarDigital Library
- Guilherme Ramos and Ludovico Boratto. 2020. Reputation (In)dependence in Ranking Systems: Demographics Influence Over Output Disparities. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25--30, 2020, Jimmy Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu (Eds.). ACM, 2061--2064. https://doi.org/10.1145/3397271.3401278Google ScholarDigital Library
- Guilherme Ramos, Ludovico Boratto, and Carlos Caleiro. 2020. On the negative impact of social influence in recommender systems: A study of bribery in collaborative hybrid algorithms. Inf. Process. Manag., Vol. 57, 2 (2020), 102058. https://doi.org/10.1016/j.ipm.2019.102058Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. AUAI Press, 452--461. https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=1&smnu=2&article_id=1630&proceeding_id=25Google Scholar
- Badrul Munir Sarwar, George Karypis, Joseph A. Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the Tenth International World Wide Web Conference, WWW 10. ACM, 285--295. https://doi.org/10.1145/371920.372071Google ScholarDigital Library
- Jo a o Saú de, Guilherme Ramos, Carlos Caleiro, and Soummya Kar. 2017. Reputation-Based Ranking Systems and Their Resistance to Bribery. In 2017 IEEE International Conference on Data Mining, ICDM 2017, New Orleans, LA, USA, November 18--21, 2017, Vijay Raghavan, Srinivas Aluru, George Karypis, Lucio Miele, and Xindong Wu (Eds.). IEEE Computer Society, 1063--1068. https://doi.org/10.1109/ICDM.2017.139Google ScholarCross Ref
- Ashudeep Singh and Thorsten Joachims. 2018. Fairness of Exposure in Rankings. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018. ACM, 2219--2228. https://doi.org/10.1145/3219819.3220088Google ScholarDigital Library
- Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu, and Bamshad Mobasher. 2020. Opportunistic Multi-aspect Fairness through Personalized Re-ranking. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2020, Genoa, Italy, July 12--18, 2020, Tsvi Kuflik, Ilaria Torre, Robin Burke, and Cristina Gena (Eds.). ACM, 239--247. https://doi.org/10.1145/3340631.3394846Google ScholarDigital Library
- Elaine Walster, Ellen Berscheid, and G William Walster. 1973. New directions in equity research. Journal of personality and social psychology, Vol. 25, 2 (1973), 151.Google ScholarCross Ref
- Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, and Ricardo Baeza-Yates. 2017. FA*IR: A Fair Top-k Ranking Algorithm. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017. ACM, 1569--1578. https://doi.org/10.1145/3132847.3132938Google ScholarDigital Library
- Meike Zehlike and Carlos Castillo. 2020. Reducing Disparate Exposure in Ranking: A Learning To Rank Approach. In WWW '20: The Web Conference 2020. ACM / IW3C2, 2849--2855. https://doi.org/10.1145/3366424.3380048Google ScholarDigital Library
Index Terms
- The Winner Takes it All: Geographic Imbalance and Provider (Un)fairness in Educational Recommender Systems
Recommendations
A Survey on the Fairness of Recommender Systems
Recommender systems are an essential tool to relieve the information overload challenge and play an important role in people’s daily lives. Since recommendations involve allocations of social resources (e.g., job recommendation), an important issue is ...
Provider fairness across continents in collaborative recommender systems
AbstractWhen a recommender system suggests items to the end-users, it gives a certain exposure to the providers behind the recommended items. Indeed, the system offers a possibility to the items of those providers of being reached and consumed ...
Highlights- We assess unfairness for groups of providers belonging to different geo-graphic continents, considering state-of-the-art recommendation models.
Feature-blind fairness in collaborative filtering recommender systems
AbstractRecommender systems were originally proposed for suggesting potentially relevant items to users, with the unique objective of providing accurate suggestions. These recommenders started being adopted in several domains, and were identified as ...
Comments