Rolling Forward: Enhancing LightGCN with Causal Graph Convolution for Credit Bond Recommendation

ArXiv ID: 2503.14213 “View on arXiv”

Authors: Unknown

Abstract

Graph Neural Networks have significantly advanced research in recommender systems over the past few years. These methods typically capture global interests using aggregated past interactions and rely on static embeddings of users and items over extended periods of time. While effective in some domains, these methods fall short in many real-world scenarios, especially in finance, where user interests and item popularity evolve rapidly over time. To address these challenges, we introduce a novel extension to Light Graph Convolutional Network (LightGCN) designed to learn temporal node embeddings that capture dynamic interests. Our approach employs causal convolution to maintain a forward-looking model architecture. By preserving the chronological order of user-item interactions and introducing a dynamic update mechanism for embeddings through a sliding window, the proposed model generates well-timed and contextually relevant recommendations. Extensive experiments on a real-world dataset from BNP Paribas demonstrate that our approach significantly enhances the performance of LightGCN while maintaining the simplicity and efficiency of its architecture. Our findings provide new insights into designing graph-based recommender systems in time-sensitive applications, particularly for financial product recommendations.

Keywords: Graph Neural Networks, Recommender Systems, Temporal Embeddings, LightGCN, Causal Convolution, Equities (Recommendation Systems)

Complexity vs Empirical Score

• Math Complexity: 5.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper uses standard Graph Neural Network equations and causal convolution formalism (moderate math), and validates the method with extensive experiments on a real-world financial dataset from BNP Paribas, reporting specific performance gains like 4x improvement in mean Average Precision.

  flowchart TD
    A["Research Goal<br>Enhance LightGCN for dynamic<br>financial bond recommendations"] --> B["Methodology<br>Integrate causal convolution with<br>LightGCN architecture"]
    B --> C["Data Input<br>BNP Paribas historical<br>user-item interaction dataset"]
    C --> D["Computational Process<br>Sliding window temporal<br>embedding updates"]
    D --> E["Key Finding 1<br>Maintains LightGCN efficiency<br>with time-sensitive modeling"]
    D --> F["Key Finding 2<br>Significant performance<br>improvement in finance domain"]