  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. From the perspective of usability, users
can delete noise and incorrect entries so that a system can provide more useful
recommendations. While unlearning is very important, it has not been
well-considered in existing recommender systems. Although there are some
researches have studied the problem of machine unlearning in the domains of
image and text data, existing methods can not been directly applied to
recommendation as they are unable to consider the collaborative information.
  In this paper, we propose RecEraser, a general and efficient machine
unlearning framework tailored to recommendation task. The main idea of
RecEraser is to partition the training set into multiple shards and train a
constituent model for each shard. Specifically, to keep the collaborative
information of the data, we first design three novel data partition algorithms
to divide training data into balanced groups based on their similarity. Then,
considering that different shard models do not uniformly contribute to the
final prediction, we further propose an adaptive aggregation method to improve
the global model utility. Experimental results on three public benchmarks show
that RecEraser can not only achieve efficient unlearning, but also outperform
the state-of-the-art unlearning methods in terms of model utility. The source
code can be found at https://github.com/chenchongthu/Recommendation-Unlearning
