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
this https URL

推荐系统在基于用户偏好的建议项目方面起着至关重要的作用。然而，在在线平台上，由于模型容量有限、数据质量差或用户兴趣的变化，这些系统不可避免地提供不适当的推荐。提高用户体验需要有效地纠正这种不适当的推荐行为。本文引入了一个名为推荐编辑的新颖且重要的任务，重点是修改已知的和不适当的推荐行为。具体而言，这个任务的目标是调整推荐模型以消除已知的不适当项目，而无需访问训练数据或重新训练模型。我们正式定义了推荐编辑的问题，有三个主要目标：严格纠正、合作纠正和集中纠正。我们开发了三个评估指标来定量评估每个目标的实现情况。我们提出了一个简单而有效的推荐编辑基准，使用了新颖的编辑贝叶斯个性化排名损失函数。为了展示所提方法的有效性，我们建立了一个综合性的基准，其中包括来自相关领域的各种方法。代码库可在此 URL 进行访问。