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of 53
pro vyhledávání: '"Abdollahpouri, Himan"'
Short- and long-term outcomes of an algorithm often differ, with damaging downstream effects. A known example is a click-bait algorithm, which may increase short-term clicks but damage long-term user engagement. A possible solution to estimate the lo
Externí odkaz:
http://arxiv.org/abs/2404.15691
Autor:
Mansoury, Masoud, Abdollahpouri, Himan, Mobasher, Bamshad, Pechenizkiy, Mykola, Burke, Robin, Sabouri, Milad
Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This is especially problematic when bias is amplified over time as a few popular items are repeatedly over
Externí odkaz:
http://arxiv.org/abs/2108.03440
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:
http://arxiv.org/abs/2107.03415
This paper proposes a vision and research agenda for the next generation of news recommender systems (RS), called the table d'hote approach. A table d'hote (translates as host's table) meal is a sequence of courses that create a balanced and enjoyabl
Externí odkaz:
http://arxiv.org/abs/2103.06909
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:
http://arxiv.org/abs/2103.06364
Recently there has been a growing interest in fairness-aware recommender systems including fairness in providing consistent performance across different users or groups of users. A recommender system could be considered unfair if the recommendations
Externí odkaz:
http://arxiv.org/abs/2008.09273
Autor:
Abdollahpouri, Himan
Traditionally, especially in academic research in recommender systems, the focus has been solely on the satisfaction of the end-user. While user satisfaction has, indeed, been associated with the success of the business, it is not the only factor. In
Externí odkaz:
http://arxiv.org/abs/2008.08551
Recommendation algorithms are known to suffer from popularity bias; a few popular items are recommended frequently while the majority of other items are ignored. These recommendations are then consumed by the users, their reaction will be logged and
Externí odkaz:
http://arxiv.org/abs/2007.13019
Popularity bias is a well-known phenomenon in recommender systems: popular items are recommended even more frequently than their popularity would warrant, amplifying long-tail effects already present in many recommendation domains. Prior research has
Externí odkaz:
http://arxiv.org/abs/2007.12230
Autor:
Abdollahpouri, Himan, Mansoury, Masoud
Academic research in recommender systems has been greatly focusing on the accuracy-related measures of recommendations. Even when non-accuracy measures such as popularity bias, diversity, and novelty are studied, it is often solely from the users' pe
Externí odkaz:
http://arxiv.org/abs/2006.15772