47 citations · 102 across the 27 of their papers we have counts for
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cs.IR2024
Towards Fairness in Provably Communication-Efficient Federated Recommender Systems
Kirandeep Kaur, Sujit Gujar, Shweta Jain
To reduce the communication overhead caused by parallel training of multiple clients, various federated learning (FL) techniques use random client sampling. Nonetheless, ensuring t…
cs.IR2021★ 5 cited
Exploring and Mitigating Gender Bias in Recommender Systems with Explicit Feedback
Shrikant Saxena, Shweta Jain
Recommender systems are indispensable because they influence our day-to-day behavior and decisions by giving us personalized suggestions. Services like Kindle, Youtube, and Netflix…
cs.IR2021★ 3 cited
An Adaptive Boosting Technique to Mitigate Popularity Bias in Recommender System
Ajay Gangwar, Shweta Jain
The observed ratings in most recommender systems are subjected to popularity bias and are thus not randomly missing. Due to this, only a few popular items are recommended, and a va…