Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Masoud Mansoury"'
Publikováno v:
ACM Transactions on Information Systems, 40(2):32. Association for Computing Machinery, Inc
Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairness where the objective is to ensure equitable coverage of items across
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5acab0b4cb9ea36d3a2c03bec69aa88e
https://doi.org/10.1145/3470948
https://doi.org/10.1145/3470948
Autor:
Himan Abdollahpouri, Shaghayegh Sahebi, Mehdi Elahi, Masoud Mansoury, Babak Loni, Zahra Nazari, Maria Dimakopoulou
Publikováno v:
Sixteenth ACM Conference on Recommender Systems.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::54aacdda0aa5044f40089851dbbb9f48
https://doi.org/10.1145/3523227.3547410
https://doi.org/10.1145/3523227.3547410
Autor:
Masoud Mansoury
Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important in multi-sided recommendation platforms where it may be crucial to optimize utilities not just for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2abea9eee44042c427545340648e1f8
http://arxiv.org/abs/2111.05564
http://arxiv.org/abs/2111.05564
Autor:
Masoud Mansoury, Weiwen Liu, Zijun Liu, Robin Burke, Shreyas Kadekodi, Andrew A. Schwartz, Ziyue Guo, Nasim Sonboli
Publikováno v:
CIKM
Recommender systems are complex. They integrate the individual needs of users with the characteristics of particular domains of application which may span items from large and potentially heterogeneous collections. Extensive experimentation is requir
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::772f6f67b7929ab5c2822256d2c64943
https://doi.org/10.1145/3459637.3482006
https://doi.org/10.1145/3459637.3482006
Autor:
Masoud Mansoury, Babak Loni, Allison J. B. Chaney, Himan Abdollahpouri, Zahra Nazari, Shaghayegh Sahebi, Mehdi Elahi
Publikováno v:
MORS@RecSys
Historically, the main criterion for a successful recommender system was the relevance of the recommended items to the user. In other words, the only objective for the recommendation algorithm was to learn user’s preferences for different items and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7285f2377466ad0cd1ecdbc54a531308
https://doi.org/10.1145/3460231.3470936
https://doi.org/10.1145/3460231.3470936
Publikováno v:
UMAP
Recommendation and ranking systems are known to suffer from popularity bias; the tendency of the algorithm to favor a few popular items while under-representing the majority of other items. Prior research has examined various approaches for mitigatin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a107274607ef10e381280f9fbefb18f5
http://arxiv.org/abs/2103.06364
http://arxiv.org/abs/2103.06364
Autor:
Masoud Mansoury
Publikováno v:
WSDM
Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important in multi-sided recommendation platforms where it may be important to optimize utilities not just f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f0c6b05f1170b2d66d30554ff0fd88e6
https://doi.org/10.1145/3437963.3441672
https://doi.org/10.1145/3437963.3441672
Publikováno v:
UMAP (Adjunct Publication)
Fairness is one of the crucial aspects of modern Recommender Systems which has recently drawn substantial attention from the community. Many recent works have addressed this aspect by studying the fairness of the recommendation through different form
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6cde83c776aff9480048bff81ef25175
https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&origin=inward&scp=85107918389
https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&origin=inward&scp=85107918389
Publikováno v:
RecSys
Comparative experimentation is important for studying reproducibility in recommender systems. This is particularly true in areas without well-established methodologies, such as fairness-aware recommendation. In this paper, we describe fairness-aware
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f66d58ab8d9dc804da5b017e81b43f3d
https://doi.org/10.1145/3383313.3411525
https://doi.org/10.1145/3383313.3411525
Publikováno v:
FAT*
The field of machine learning fairness has developed metrics, methodologies, and data sets for experimenting with classification algorithms. However, equivalent research is lacking in the area of personalized recommender systems. This 180-minute hand
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::28809374c757d0ca6a14f327e7046306
https://doi.org/10.1145/3351095.3375670
https://doi.org/10.1145/3351095.3375670