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

• URL: http://arxiv.org/abs/2503.14213v1
• Date: Tue, 18 Mar 2025 12:47:01 GMT
• Title: Rolling Forward: Enhancing LightGCN with Causal Graph Convolution for Credit Bond Recommendation
• Authors: Ashraf Ghiye, Baptiste Barreau, Laurent Carlier, Michalis Vazirgiannis,
• Abstract summary: We introduce a novel extension to Light Graph Convolutional Network (LightGCN) designed to learn temporal node embeddings that capture dynamic interests.<n>Our approach employs causal convolution to maintain a forward-looking model architecture.<n>Our findings provide new insights into designing graph-based recommender systems in time-sensitive applications.
• Score: 16.015835475954773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation [1.0128808054306186]
  We introduce the Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation (ALDA4Rec) method. ALDA4Rec is a novel model that constructs an item-item graph, filters noise through community detection, and enriches user-item interactions. Experiments conducted on four real-world datasets demonstrate that ALDA4Rec outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-04-18T10:42:16Z)
• Recommendation System in Advertising and Streaming Media: Unsupervised Data Enhancement Sequence Suggestions [2.9633211091806997]
  We introduce a novel framework, Global Unsupervised Data-Augmentation (UDA4SR), which adopts a graph contrastive learning perspective to generate robust item embeddings for sequential recommendation. Our approach begins by integrating Generative Adrial Networks (GANs) for data augmentation, which serves as the first step to enhance the diversity and richness of the training data. To model users' dynamic and diverse interests more effectively, we enhance the CapsNet module with a novel target-attention mechanism.
  arXiv  Detail & Related papers  (2025-03-23T06:30:48Z)
• Graph Foundation Models for Recommendation: A Comprehensive Survey [55.70529188101446]
  Large language models (LLMs) are designed to process and comprehend natural language, making both approaches highly effective and widely adopted.<n>Recent research has focused on graph foundation models (GFMs)<n>GFMs integrate the strengths of GNNs and LLMs to model complex RS problems more efficiently by leveraging the graph-based structure of user-item relationships alongside textual understanding.
  arXiv  Detail & Related papers  (2025-02-12T12:13:51Z)
• Linear-Time Graph Neural Networks for Scalable Recommendations [50.45612795600707]
  The key of recommender systems is to forecast users' future behaviors based on previous user-item interactions. Recent years have witnessed a rising interest in leveraging Graph Neural Networks (GNNs) to boost the prediction performance of recommender systems. We propose a Linear-Time Graph Neural Network (LTGNN) to scale up GNN-based recommender systems to achieve comparable scalability as classic MF approaches.
  arXiv  Detail & Related papers  (2024-02-21T17:58:10Z)
• Multi-Scene Generalized Trajectory Global Graph Solver with Composite Nodes for Multiple Object Tracking [61.69892497726235]
  Composite Node Message Passing Network (CoNo-Link) is a framework for modeling ultra-long frames information for association. In addition to the previous method of treating objects as nodes, the network innovatively treats object trajectories as nodes for information interaction. Our model can learn better predictions on longer-time scales by adding composite nodes.
  arXiv  Detail & Related papers  (2023-12-14T14:00:30Z)
• GraphPro: Graph Pre-training and Prompt Learning for Recommendation [18.962982290136935]
  GraphPro is a framework that incorporates parameter-efficient and dynamic graph pre-training with prompt learning. Our framework addresses the challenge of evolving user preferences by seamlessly integrating a temporal prompt mechanism and a graph-structural prompt learning mechanism.
  arXiv  Detail & Related papers  (2023-11-28T12:00:06Z)
• Self-supervised Graph-based Point-of-interest Recommendation [66.58064122520747]
  Next Point-of-Interest (POI) recommendation has become a prominent component in location-based e-commerce. We propose a Self-supervised Graph-enhanced POI Recommender (S2GRec) for next POI recommendation. In particular, we devise a novel Graph-enhanced Self-attentive layer to incorporate the collaborative signals from both global transition graph and local trajectory graphs.
  arXiv  Detail & Related papers  (2022-10-22T17:29:34Z)
• Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation [57.67616822888859]
  We propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging Reinforcement Learning (TMER-RL) TMER-RL utilizes reinforcement item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendation. Extensive evaluations of TMER on two real-world datasets show state-of-the-art performance compared against recent strong baselines.
  arXiv  Detail & Related papers  (2021-11-24T04:34:26Z)
• Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation [62.30552176649873]
  We devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe) To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice. To enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices.
  arXiv  Detail & Related papers  (2021-09-24T07:44:27Z)
• Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations [3.286961611175469]
  We propose a new deep learning-based sequential recommendation approach based on a Position-enhanced and Time-aware Graph Convolutional Network (PTGCN) PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation. It realizes the high-order connectivity between users and items by stacking multi-layer graph convolutions.
  arXiv  Detail & Related papers  (2021-07-12T07:34:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.