Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects
Personal digital data is a critical asset, and governments worldwide have enforced laws and
regulations to protect data privacy. Data users have been endowed with the right to be …
regulations to protect data privacy. Data users have been endowed with the right to be …
A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions
Y Li, X Feng, C Chen, Q Yang - arXiv preprint arXiv:2412.12836, 2024 - arxiv.org
Recommender systems have become increasingly influential in shaping user behavior and
decision-making, highlighting their growing impact in various domains. Meanwhile, the …
decision-making, highlighting their growing impact in various domains. Meanwhile, the …
Machine unlearning for recommendation systems: An insight
This review explores machine unlearning (MUL) in recommendation systems, addressing
adaptability, personalization, privacy, and bias challenges. Unlike traditional models, MUL …
adaptability, personalization, privacy, and bias challenges. Unlike traditional models, MUL …
Efficient and Adaptive Recommendation Unlearning: A Guided Filtering Framework to Erase Outdated Preferences
Recommendation unlearning is an emerging task to erase the influences of user-specified
data from a trained recommendation model. Most existing research follows the paradigm of …
data from a trained recommendation model. Most existing research follows the paradigm of …
Age Ain't Just a Number: Exploring the Volume vs. Age Dilemma for Textual Data to Enhance Decision Making
L Hägele, M Klier, A Obermeier, T Widmann - 2024 - aisel.aisnet.org
The common belief that more data leads to better results often leads to all available data
being used to derive the best possible decision. However, the age of data can strongly affect …
being used to derive the best possible decision. However, the age of data can strongly affect …