Modeling Endogenous Logic: Causal Neuro-Symbolic Reasoning Model for Explainable Multi-Behavior Recommendation

TL;DR

This work tackles explainability in multi-behavior recommendations by introducing CNRE, a causal neuro-symbolic framework that derives endogenous logic from user behavior chains. It employs Front-door Adjustment, simulated via neural-symbolic mediation, to produce a tractable mediator $M$ and route reasoning along strong, medium, or weak preference paths, yielding $P(Y|do(M))$-driven predictions. The approach integrates Hierarchical Preference Propagation with adaptive projection to handle behavior heterogeneity and confounding, delivering multi-level explainability from model design to decision process and results, while outperforming state-of-the-art baselines on three public datasets. CNRE achieves these gains with self-contained reasoning that does not rely on external attributes, offering improved generalizability and actionable explanations for real-world recommender systems.

Abstract

Existing multi-behavior recommendations tend to prioritize performance at the expense of explainability, while current explainable methods suffer from limited generalizability due to their reliance on external information. Neuro-Symbolic integration offers a promising avenue for explainability by combining neural networks with symbolic logic rule reasoning. Concurrently, we posit that user behavior chains inherently embody an endogenous logic suitable for explicit reasoning. However, these observational multiple behaviors are plagued by confounders, causing models to learn spurious correlations. By incorporating causal inference into this Neuro-Symbolic framework, we propose a novel Causal Neuro-Symbolic Reasoning model for Explainable Multi-Behavior Recommendation (CNRE). CNRE operationalizes the endogenous logic by simulating a human-like decision-making process. Specifically, CNRE first employs hierarchical preference propagation to capture heterogeneous cross-behavior dependencies. Subsequently, it models the endogenous logic rule implicit in the user's behavior chain based on preference strength, and adaptively dispatches to the corresponding neural-logic reasoning path (e.g., conjunction, disjunction). This process generates an explainable causal mediator that approximates an ideal state isolated from confounding effects. Extensive experiments on three large-scale datasets demonstrate CNRE's significant superiority over state-of-the-art baselines, offering multi-level explainability from model design and decision process to recommendation results.

Figures (4)

• Figure 1: Illustration of the causal graph for Front-door Adjustment.
• Figure 2: Illustration of our proposed CNRE model.
• Figure 3: Analysis of decision process explainability.
• Figure 4: Robustness and cascading effectiveness analysis.