Rolling Forward: Enhancing LightGCN with Causal Graph Convolution for Credit Bond Recommendation
This work addresses the need for dynamic recommendations in time-sensitive financial applications, offering an incremental improvement over existing methods.
The paper tackled the problem of static embeddings in graph neural networks for recommender systems, which fail in time-sensitive domains like finance, by introducing a novel extension to LightGCN that uses causal convolution and dynamic updates to capture evolving interests, resulting in significant performance enhancements on a real-world dataset from BNP Paribas.
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.