Graph Convolutional Matrix Completion
read the original abstract
We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings. Building on recent progress in deep learning on graph-structured data, we propose a graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph. Our model shows competitive performance on standard collaborative filtering benchmarks. In settings where complimentary feature information or structured data such as a social network is available, our framework outperforms recent state-of-the-art methods.
This paper has not been read by Pith yet.
Forward citations
Cited by 8 Pith papers
-
Efficient Retrieval Scaling with Hierarchical Indexing for Large Scale Recommendation
A jointly learned hierarchical index with cross-attention and residual quantization scales exact retrieval in foundational recommendation models, deployed at Meta with additional performance from test-time training on...
-
Graph-Based Alternatives to LLMs for Human Simulation
GEMS formulates close-ended human-behavior simulation as link prediction on a heterogeneous graph and matches or exceeds LLM performance with three orders of magnitude fewer parameters across three datasets and three ...
-
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
DGL is a graph-centric library that optimizes GNNs via generalized sparse tensor operations, transparent graph-based optimizations, and framework-neutral design, claiming superior speed and memory use over other GNN f...
-
Regional based query in graph active learning
Proposes regional uncertainty and page-rank extended query selection for active learning on graphs, claiming superiority over standard methods at different labeling densities.
-
PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems
PBiLoss is a model-agnostic regularization loss with PopPos and PopNeg sampling that reduces popularity bias metrics PRU and PRI by up to 10% in GNN recommenders while preserving accuracy on datasets like MovieLens.
-
Disentangling Popularity and Quality: An Edge Classification Approach for Fair Recommendation
GNN recommender uses edge classification and cost-sensitive learning to disentangle popularity bias from quality, reporting ~32% average fairness gains with competitive accuracy.
-
Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation
ALDA4Rec improves sequential recommendation by denoising item-item graphs via community detection and adaptively fusing short-term GCN embeddings with long-term sequence models using GRUs, attention, and MLP weighting.
-
Deep Social Collaborative Filtering
DSCF is a deep social collaborative filtering model that uses distant neighbors and item-relevant opinions from social networks to improve recommendation accuracy over prior deep models.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.