用于推荐解释的反事实画像扰动：一项可复现性研究
Counterfactual Profile Perturbations for Recommendation Explanations: A Reproducibility Study
Meysam Varasteh · Elizabeth McKinnie · Robin Burke
中文摘要
该可复现性研究探讨了名为 LXR 的反事实解释技术。先前工作表明，LXR 能可靠地识别出用户画像中的扰动，使原本排名最高的物品不再被推荐，从而解释推荐结果。本研究扩展了 LXR 的应用范围，发现其对非最高排名物品的解释同样有效。研究还评估了物品流行度和潜在因子相似度等替代基线，发现某些任务下这些基线表现优异。此外，分析显示 LXR 在解释低流行度物品时能提供更短、质量更高的扰动。最后，通过暴力搜索扰动空间子集，估算了最优扰动大小的下界，为 LXR 等方法的性能比较提供了基准。
关键要点
- 01验证 LXR 在解释非最高排名推荐物品时的有效性，扩展了原研究的应用范围。
- 02对比物品流行度与潜在因子相似度基线，发现部分任务中替代方法表现优于 LXR。
- 03发现 LXR 在解释低流行度物品时能生成更短的反事实扰动，解释质量更高。
- 04通过暴力搜索扰动空间子集，估算最优扰动大小下界，为方法评估提供基准。
解读
尚无解读。
原始英文摘要
arXiv:2610.36208v1 Announce Type: cross Abstract: This reproducibility paper explores the counterfactual explanation technique known as Learning to eXplain Recommendations (LXR). Prior work has shown that LXR can reliably identify perturbations that, if applied to a user's profile, cause the user's top ranked item to no longer be recommended, thereby explaining the item's recommendation as a function of the user's rating choices. In this paper, we extend this work in multiple ways. We expand the scope of the original LXR study to look at explanations for items other than the most highly recommended item, finding that LXR is also effective at this version of the task. We consider alternate baselines for LXR, including item popularity and latent factor similarity, and we see that for some tasks these alternatives work surprisingly well. We also examine the impact of item popularity, finding that LXR offers better explanation quality (shorter perturbations) when less popular items are being explained. Finally, we conduct a brute-force exploration of a subset of the perturbation space in order to estimate a lower bound for optimal perturbation size against which LXR and other such methods can be compared.