  Recommendation systems play a pivotal role in suggesting items to users based
on their preferences. However, in online platforms, these systems inevitably
offer unsuitable recommendations due to limited model capacity, poor data
quality, or evolving user interests. Enhancing user experience necessitates
efficiently rectify such unsuitable recommendation behaviors. This paper
introduces a novel and significant task termed recommendation editing, which
focuses on modifying known and unsuitable recommendation behaviors.
Specifically, this task aims to adjust the recommendation model to eliminate
known unsuitable items without accessing training data or retraining the model.
We formally define the problem of recommendation editing with three primary
objectives: strict rectification, collaborative rectification, and concentrated
rectification. Three evaluation metrics are developed to quantitatively assess
the achievement of each objective. We present a straightforward yet effective
benchmark for recommendation editing using novel Editing Bayesian Personalized
Ranking Loss. To demonstrate the effectiveness of the proposed method, we
establish a comprehensive benchmark that incorporates various methods from
related fields. Codebase is available at
https://github.com/cycl2018/Recommendation-Editing.
