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

arXiv: https://arxiv.org/abs/2601.21335 · HTML: https://mlsanity.com/paper/2601.21335/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning.
- Builds on 24 of its 49 references that are in the map.
- First posted on arXiv 29 Jan 2026.

## Placed in

- LLM reasoning / [LLM reasoning for recommendation](https://mlsanity.com/lr/llm-reasoning-for-recommendation/)

## Builds on (most cited first, 24 of 24)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://mlsanity.com/paper/1708.04617/) (2017)
- [Self-Supervised Learning for Recommender Systems: A Survey](https://mlsanity.com/paper/2203.15876/) (2022)
- [Personalized Prompt Learning for Explainable Recommendation](https://mlsanity.com/paper/2202.07371/) (2022)
- [Counterfactual Explainable Recommendation](https://mlsanity.com/paper/2108.10539/) (2021)
- [Causal Inference in Recommender Systems: A Survey and Future Directions](https://mlsanity.com/paper/2208.12397/) (2022)
- [Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation](https://mlsanity.com/paper/2207.05584/) (2022)
- [A Survey on Neural-symbolic Learning Systems](https://mlsanity.com/paper/2111.08164/) (2021)
- [Neural Logic Reasoning](https://mlsanity.com/paper/2008.09514/) (2020)
- [Joint Optimization of Tree-based Index and Deep Model for Recommender Systems](https://mlsanity.com/paper/1902.07565/) (2019)
- [CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation](https://mlsanity.com/paper/2010.15620/) (2020)
- [Unlocking the Potential of Large Language Models for Explainable Recommendations](https://mlsanity.com/paper/2312.15661/) (2023)
- [Deconfounded Causal Collaborative Filtering](https://mlsanity.com/paper/2110.07122/) (2021)
- [Addressing Confounding Feature Issue for Causal Recommendation](https://mlsanity.com/paper/2205.06532/) (2022)
- [Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation](https://mlsanity.com/paper/2205.13128/) (2022)
- [Multi-behavior Self-supervised Learning for Recommendation](https://mlsanity.com/paper/2305.18238/) (2023)
- [Mitigating Hidden Confounding Effects for Causal Recommendation](https://mlsanity.com/paper/2205.07499/) (2022)
- [Multi-Behavior Recommendation with Cascading Graph Convolution Networks](https://mlsanity.com/paper/2303.15720/) (2023)
- [Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation](https://mlsanity.com/paper/2404.18166/) (2024)
- [Neuro-Symbolic Recommendation Model based on Logic Query](https://mlsanity.com/paper/2309.07594/) (2023)
- [Causal Distillation for Alleviating Performance Heterogeneity in Recommender Systems](https://mlsanity.com/paper/2405.20626/) (2024)
- [Neutralizing Popularity Bias in LLM-based Recommendation via Counterfactual Reasoning Guidelines](https://mlsanity.com/paper/2503.08051/) (2025)

## Abstract (arXiv)

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.