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.

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