BM
Bamshad Mobasher
4 records found
1
Mitigating Exposure Bias in Online Learning to Rank Recommendation
A Novel Reward Model for Cascading Bandits
Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This bias becomes particularly problematic over time as a few items are repeatedly over-represented in recommendation lists, leading to
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Beyond Static Calibration
The Impact of User Preference Dynamics on Calibrated Recommendation
Calibration in recommender systems is an important performance criterion that ensures consistency between the distribution of user preference categories and that of recommendations generated by the system. Standard methods for mitigating miscalibration typically assume that user
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ComplexRec 2021
Fifth workshop on recommendation in complex environments
During the past decade, recommender systems have rapidly become an indispensable element of websites, apps, and other platforms that seek to provide personalized interactions to their users. As recommendation technologies are applied to an ever-growing array of non-standard probl
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UMAP 2019 theory, reflection, and opinion track
Chairs' welcome and overview
ACM UMAP - User Modelling, Adaptation and Personalization is the premier international conference for researchers and practitioners working on systems that adapt to individual users, to groups of users, and that collect, represent, and model user information. The Theory, Opinion
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