Dimensions overall score 8.0

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• Existing multi-behavior recommendation methods prioritize performance over explainability.limitation 85%
• Current explainable recommendation methods have limited generalizability due to reliance on external information.limitation 80%
• User behavior chains inherently embody endogenous logic suitable for explicit reasoning.technical 75%
• Observational multiple behaviors are plagued by confounders that cause models to learn spurious correlations.technical 80%
• CNRE employs hierarchical preference propagation to capture heterogeneous cross-behavior dependencies.method 90%
• CNRE models endogenous logic rules based on preference strength and adaptively dispatches to neural-logic reasoning paths.method 90%
• CNRE generates an explainable causal mediator that approximates an ideal state isolated from confounding effects.method 85%
• CNRE demonstrates significant superiority over state-of-the-art baselines on three large-scale datasets.result 90%

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