2022

Recommendation Unlearning

Chen, Chong, Sun, Fei, Zhang, Min et al.

Understand

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 data.
From the perspective of privacy, several privacy regulations have recently been proposed, requiring systems to eliminate any impact of the data whose owner requests to forget.
From the perspective of utility, if a system's utility is damaged by some bad data, the system needs to forget these data to regain utility.

Built on

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Then

Efficient Neural Matrix Factorization without Sampling for Recommendation
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