Graph Convolutional Matrix Completion for Bipartite Edge Prediction.
Yuexin Wu,Hanxiao Liu,Yiming Yang +2 more
- pp 49-58
About:
This article is published in International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management.The article was published on 2018-01-01 and is currently open access. It has received 45 citations till now. The article focuses on the topics: Bipartite graph & Graph (abstract data type).read more
Citations
More filters
Proceedings ArticleDOI
A Neural Influence Diffusion Model for Social Recommendation
TL;DR: Zhang et al. as discussed by the authors proposed a deep influence propagation model to stimulate how users are influenced by the recursive social diffusion process for social recommendation, which can be applied when the user~(item) attributes or the social network structure is not available.
Proceedings ArticleDOI
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
TL;DR: In this paper, the authors proposed a knowledge-aware graph neural network with label smoothness regularization (KGNN-LS) to compute personalized item embeddings by first applying a trainable function that identifies important knowledge graph relationships for a given user and then transform the knowledge graph into a user-specific weighted graph.
Posted Content
Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
TL;DR: This paper revisits GCN based CF models from two aspects and proposes a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user- item interaction data.
Posted Content
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
TL;DR: This work proposes Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS), which relies on label smoothness assumption, which posits that adjacent items in the knowledge graph are likely to have similar user relevance labels/scores, and proves that it is equivalent to a label propagation scheme on a graph.
Posted Content
A Neural Influence Diffusion Model for Social Recommendation
TL;DR: A deep influence propagation model is proposed to stimulate how users are influenced by the recursive social diffusion process for social recommendation, with more than 13% performance improvements over the best baselines for top-10 recommendation on the two datasets.
References
More filters
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Journal ArticleDOI
Exact Matrix Completion via Convex Optimization
TL;DR: It is proved that one can perfectly recover most low-rank matrices from what appears to be an incomplete set of entries, and that objects other than signals and images can be perfectly reconstructed from very limited information.
Proceedings Article
Probabilistic Matrix Factorization
Andriy Mnih,Ruslan Salakhutdinov +1 more
TL;DR: The Probabilistic Matrix Factorization (PMF) model is presented, which scales linearly with the number of observations and performs well on the large, sparse, and very imbalanced Netflix dataset and is extended to include an adaptive prior on the model parameters.
Journal ArticleDOI
The MovieLens Datasets: History and Context
TL;DR: The history of MovieLens and the MovieLens datasets is documents, including a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization, and best practices and limitations of using the Movie Lens datasets in new research are documented.
Journal ArticleDOI
Graph Regularized Nonnegative Matrix Factorization for Data Representation
TL;DR: In GNMF, an affinity graph is constructed to encode the geometrical information and a matrix factorization is sought, which respects the graph structure, and the empirical study shows encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-world problems.