arXiv cs.AI· Meysam Varasteh, Elizabeth McKinnie, Robin Burke·· 13 小时前AI 评分18
LXR 反事实推荐解释的可复现性研究：扩展至非首位推荐项与流行度影响
Counterfactual Profile Perturbations for Recommendation Explanations: A Reproducibility Study
AI 导读
可复现性研究对反事实解释方法 LXR 进行扩展验证，发现 LXR 在解释非最高排名推荐项时同样有效。研究引入物品流行度和隐因子相似度作为替代基线，部分任务上表现意外出色；LXR 在解释低流行度物品时扰动更短、解释质量更高。作者还对部分扰动空间做暴力搜索，估算最优扰动规模下界以供后续方法比较。
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