TY - CHAP

T1 - Modeling endogenous logic: Causal neuro-symbolic reasoning model for explainable multi-behavior recommendation

AU - Chen, Y.

AU - Cao, J.

AU - Wang, Youquan

AU - Tao, Haicheng

AU - Вукович, Дарко

AU - Wu, Jia

N1 - Chen, Y., Cao, J., Wang, Y., Tao, H., Vukovic, D. B., & Wu, J. (2026). Modeling endogenous logic: Causal neuro-symbolic reasoning model for explainable multi-behavior recommendation. In Proceedings of the ACM Web Conference 2026 (pp. 6400–6410). Association for Computing Machinery. https://doi.org/10.1145/3774904.3792419

PY - 2026/4/12

Y1 - 2026/4/12

N2 - 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 (e.g., view-→cart-→buy) 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.

AB - 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 (e.g., view-→cart-→buy) 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.

KW - causal inference

KW - explainable recommendation

KW - multi-behavior recommendation

KW - neuro-symbolic reasoning

UR - https://dl.acm.org/doi/10.1145/3774904.3792419

UR - https://www.mendeley.com/catalogue/f56f0d4e-ef5c-3756-8ab7-cad8bb210064/

U2 - 10.1145/3774904.3792419

DO - 10.1145/3774904.3792419

M3 - Chapter

SN - 9798400723070

T3 - IEEE transactions on big data

SP - 6400

EP - 6410

BT - WWW 2026 - Proceedings of the ACM Web Conference 2026

PB - Association for Computing Machinery

CY - New York, USA

T2 - ACM Web Conference 2026

Y2 - 13 April 2026 through 17 April 2026

ER -