5 citations · 6 across the 4 of their papers we have counts for
4 papers
Selective and Collaborative Influence Function for Efficient Recommendation Unlearning
Yuyuan Li, Chaochao Chen, Xiaolin Zheng +3
Recent regulations on the Right to be Forgotten have greatly influenced the way of running a recommender system, because users now have the right to withdraw their private data. Be…
CryptoRec: Privacy-preserving Recommendation as a Service
Jun Wang, Afonso Arriaga, Qiang Tang +1
Recommender systems rely on large datasets of historical data and entail serious privacy risks. A server offering Recommendation as a Service to a client might leak more informatio…
A Probabilistic View of Neighborhood-based Recommendation Methods
Jun Wang, Qiang Tang
Probabilistic graphic model is an elegant framework to compactly present complex real-world observations by modeling uncertainty and logical flow (conditionally independent factors…
Differentially Private Neighborhood-based Recommender Systems
Jun Wang, Qiang Tang
Privacy issues of recommender systems have become a hot topic for the society as such systems are appearing in every corner of our life. In contrast to the fact that many secure mu…