DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation

Abstract

Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy. Representing user-item interactions as a bipartite graph, a GNN model generates user and item representations by aggregating embeddings of their neighbors. However, such an aggregation procedure often accumulates information purely based on the graph structure, overlooking the redundancy of the aggregated neighbors and resulting in poor diversity of the recommended list. In this paper, we propose diversifying GNN-based recommender systems by directly improving the embedding generation procedure. Particularly, we utilize the following three modules: submodular neighbor selection to find a subset of diverse neighbors to aggregate for each GNN node, layer attention to assign attention weights for each layer, and loss reweighting to focus on the learning of items belonging to long-tail categories. Blending the three modules into GNN, we present DGRec(Diversified GNN-based Recommender System) for diversified recommendation. Experiments on real-world datasets demonstrate that the proposed method can achieve the best diversity while keeping the accuracy comparable to state-of-the-art GNN-based recommender systems.

arXiv comment9 pages, WSDM 2023

References in corpus (23)

• Adam: A Method for Stochastic Optimization
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Neural Graph Collaborative Filtering
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• Graph Convolutional Matrix Completion
• Solving the apparent diversity-accuracy dilemma of recommender systems
• Towards Deeper Graph Neural Networks
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
• NPA: Neural News Recommendation with Personalized Attention
• Federated Social Recommendation with Graph Neural Network
• Spectral Collaborative Filtering
• ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation
• DGCN: Diversified Recommendation with Graph Convolutional Networks
• Understanding Echo Chambers in E-commerce Recommender Systems
• FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
• Adaptive, Personalized Diversity for Visual Discovery
• Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network
• BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
• Recommending Accurate and Diverse Items Using Bilateral Branch Network
• UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
• Sliding Spectrum Decomposition for Diversified Recommendation
• Semi-supervised Embedding in Attributed Networks with Outliers
• ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation

Cited by in corpus (5)

• Graph-based Alignment and Uniformity for Recommendation
• Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering
• Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video Recommendation
• Relevance meets Diversity: A User-Centric Framework for Knowledge Exploration through Recommendations
• Category-based and Popularity-guided Video Game Recommendation: A Balance-oriented Framework