262 citations · 1.1k across the 41 of their papers we have counts for
5 papers · 1 filter
LegoNet: A Fast and Exact Unlearning Architecture
Sihao Yu, Fei Sun, Jiafeng Guo +2
Machine unlearning aims to erase the impact of specific training samples upon deleted requests from a trained model. Re-training the model on the retained data after deletion is an…
Debiasing Learning for Membership Inference Attacks Against Recommender Systems
Zihan Wang, Na Huang, Fei Sun +5
Learned recommender systems may inadvertently leak information about their training data, leading to privacy violations. We investigate privacy threats faced by recommender systems…
Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation
Ziqian Chen, Fei Sun, Yifan Tang +3
Recently, privacy issues in web services that rely on users' personal data have raised great attention. Unlike existing privacy-preserving technologies such as federated learning a…
Neural Re-ranking in Multi-stage Recommender Systems: A Review
Weiwen Liu, Yunjia Xi, Jiarui Qin +5
As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects user experience and satisfaction by rearranging the input ranking lists, and thereby pla…
Recommendation Unlearning
Chong Chen, Fei Sun, Min Zhang +1
Recommender systems provide essential web services by learning users' personal preferences from collected data. However, in many cases, systems also need to forget some training da…