论文标题
推荐系统的顺序性质破坏了评估过程
Sequential Nature of Recommender Systems Disrupts the Evaluation Process
论文作者
论文摘要
数据集通常是按顺序生成的，在先前的样本和中间决策或干预措施会影响后续样本。在存在重大人类相互作用（例如推荐系统中）的情况下，这尤其突出。为了表征这种关系在样本中的重要性，我们建议在流行的评估过程中使用对抗性攻击。我们提出了序列意识的提升攻击，并为仅根据观察到的数据的顺序从机密测试集中利用的额外信息量提供了下限。我们使用真实和合成数据来测试我们的方法，并表明Movielense-100K数据集上的评估过程可能会受到$ \ sim1 \％$的影响，这在考虑近距离竞争时很重要。代码公开可用。
Datasets are often generated in a sequential manner, where the previous samples and intermediate decisions or interventions affect subsequent samples. This is especially prominent in cases where there are significant human-AI interactions, such as in recommender systems. To characterize the importance of this relationship across samples, we propose to use adversarial attacks on popular evaluation processes. We present sequence-aware boosting attacks and provide a lower bound on the amount of extra information that can be exploited from a confidential test set solely based on the order of the observed data. We use real and synthetic data to test our methods and show that the evaluation process on the MovieLense-100k dataset can be affected by $\sim1\%$ which is important when considering the close competition. Codes are publicly available.
