AI 中文总结
研究推荐系统事后可解释性的可扩展性问题，提出用谱双聚类驱动的块删除诊断框架，通过对用户和项目分组并删除交互块减少重新训练次数，在两种推荐范式和两个数据集上评估，揭示推荐对交互块的敏感性及用户段差异，提供实用分析框架。
AI 中文摘要
推荐系统中的可解释性对于确保透明度、问责制和信任至关重要，但现有的事后方法往往面临严重的可扩展性挑战。观察级删除诊断提供了一种反事实方法，通过在删除单个用户或项目后重新训练模型来分析推荐，但成本随数据集大小迅速增长。为提高此分析的实际可处理性，本文引入了一个块删除诊断框架，该框架使用谱双聚类对用户和项目进行分组，然后删除整个交互块。相对于更细粒度的删除策略，这种方法减少了重新训练过程的数量，并在用户段、项目组及其交互层面产生解释。该框架在奇异值分解和神经协同过滤这两种代表性推荐范式上进行评估，使用MovieLens和亚马逊数据集。结果表明，排名靠前的推荐通常比排名靠后的推荐对特定交互块更敏感，一些块起支持作用，而另一些对推荐质量有不利影响。分析还表明用户段对块删除的敏感度不同，这表明对局部交互模式的依赖程度存在差异。这些发现提供了通过标准推荐指标无法直接看到的诊断信息。总体而言，结果表明块删除诊断为推荐系统提供了一个实用且与模型无关的事后分析框架，同时也强调了所得解释取决于所选的块结构。
英文摘要
Explainability in recommender systems is essential for ensuring transparency, accountability, and trust, yet existing post-hoc methods often encounter severe scalability challenges. Observation-level deletion diagnostics offer a counterfactual way to analyze recommendations by retraining models after removing individual users or items, but their cost grows rapidly with dataset size. To improve the practical tractability of this analysis, this paper introduces a block-deletion diagnostic framework that uses spectral biclustering to group users and items and then removes entire blocks of interactions. This formulation reduces the number of retraining procedures relative to finer-grained deletion strategies and produces explanations at the level of user segments, item groups, and their interactions. The framework is evaluated on two representative recommender paradigms, Singular Value Decomposition and Neural Collaborative Filtering, using the MovieLens and Amazon datasets. The results show that top-ranked recommendations are often more sensitive to specific interaction blocks than lower-ranked ones, with some blocks acting as supporting evidence and others having a detrimental effect on recommendation quality. The analysis also indicates that user segments differ in their sensitivity to block removal, suggesting heterogeneous levels of reliance on localized interaction patterns. These findings provide diagnostic information that is not directly visible through standard recommendation metrics. Overall, the results suggest that block-deletion diagnostics offer a practical and model-agnostic post-hoc analysis framework for recommender systems, while also highlighting that the resulting explanations depend on the chosen block structure.
Journal refKnow.-Based Syst. 342, C (Jun 2026)
DOI：10.1016/j.knosys.2026.115904
