The content discusses the limitations of attention-based explainability in recommendation models and introduces a novel framework based on counterfactual reasoning for path-based explanations. It compares the proposed method with traditional attention mechanisms, highlighting stability, effectiveness, and qualitative evaluations.
The study evaluates the proposed method through quantitative metrics such as confidence, informativeness, and fidelity. It also includes qualitative assessments like stability, effectiveness, and visualization through case studies. Additionally, it analyzes the reinforcement learning-based path manipulation and the overall performance of the recommendation model.
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