反事实画像扰动用于推荐解释：LXR 可复现性研究
Counterfactual Profile Perturbations for Recommendation Explanations: A Reproducibility Study
AI 导读
一篇可复现性论文对推荐反事实解释方法 LXR（Learning to eXplain Recommendations）进行了扩展验证。研究将 LXR 应用到非最高推荐项的解释任务并确认其有效，同时测试物品流行度和潜在因子相似度等替代基线，发现部分任务上这些基线表现意外地好；LXR 在解释冷门物品时产生更短的扰动，作者还通过暴力枚举部分扰动空间估计了最优扰动大小的下界。
正文 · 原文
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.
| Subjects: | Information Retrieval (cs.IR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.36208 [cs.IR] |
| (or arXiv:2610.36208v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36208 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Meysam Varasteh [view email]
[v1]
Mon, 26 Oct 2026 19:43:35 UTC (70 KB)
来源：arXiv cs.AI · arxiv.org