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Material similarity functions as the implicit premise underlying domain adaptation in materials informatics, yet it remains largely undefined, rendering transfer claims difficult to evaluate and compare. This work establishes a task-dependent, operational definition of material similarity for composition-transfer learning, grounded in four orthogonal dimensions: composition, structure, property behavior, and local atomic environment. By examining current usage patterns, it reveals systematic ambiguities arising from single-dimension interpretations and demonstrates how these ambiguities contribute to inconsistent transfer outcomes. The proposed framework introduces threshold-based criteria and a weighted similarity score that jointly determine when transfer is justified, alongside boundary conditions that constrain successful adaptation. Gray zones and edge cases are analyzed to show how trade-offs across dimensions must be explicitly quantified rather than implicitly assumed. The resulting formulation repositions similarity as a measurable, task-specific precondition rather than a descriptive label, enabling pre-transfer validation and standardized reporting. This shift provides a conceptual foundation for reproducible domain adaptation, aligning materials machine learning with more rigorous principles of transferability and model generalization.