MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering
arXiv:2204.02338 · doi:10.1109/TKDE.2023.3348537
Abstract
Graph Neural Networks (GNNs) have recently been utilized to build Collaborative Filtering (CF) models to predict user preferences based on historical user-item interactions. However, there is relatively little understanding of how GNN-based CF models relate to some traditional Network Representation Learning (NRL) approaches. In this paper, we show the equivalence between some state-of-the-art GNN-based CF models and a traditional 1-layer NRL model based on context encoding. Based on a Markov process that trades off two types of distances, we present Markov Graph Diffusion Collaborative Filtering (MGDCF) to generalize some state-of-the-art GNN-based CF models. Instead of considering the GNN as a trainable black box that propagates learnable user/item vertex embeddings, we treat GNNs as an untrainable Markov process that can construct constant context features of vertices for a traditional NRL model that encodes context features with a fully-connected layer. Such simplification can help us to better understand how GNNs benefit CF models. Especially, it helps us realize that ranking losses play crucial roles in GNN-based CF tasks. With our proposed simple yet powerful ranking loss InfoBPR, the NRL model can still perform well without the context features constructed by GNNs. We conduct experiments to perform detailed analysis on MGDCF.
arXiv commentAccepted by IEEE Transactions on Knowledge and Data Engineering (TKDE)
References in corpus (16)
- DeepWalk: Online Learning of Social Representations
- Representation Learning with Contrastive Predictive Coding
- LINE: Large-scale Information Network Embedding
- BPR: Bayesian Personalized Ranking from Implicit Feedback
- Neural Graph Collaborative Filtering
- Graph Convolutional Neural Networks for Web-Scale Recommender Systems
- Self-supervised Graph Learning for Recommendation
- Graph Convolutional Matrix Completion
- Representation Learning on Graphs with Jumping Knowledge Networks
- DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
- Predict then Propagate: Graph Neural Networks meet Personalized PageRank
- LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
- Diffusion Improves Graph Learning
- Tackling Over-Smoothing for General Graph Convolutional Networks
- GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation
- UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation