GCN-ICF: Graph Convolution Networks for Item-based Recommendation.
Kai DengJiajin HuangJin QinPublished in: WI/IAT (2020)
Keyphrases
- collaborative filtering
- recommender systems
- heterogeneous social networks
- image processing
- highly connected
- average degree
- fully connected
- edge weights
- social networks
- structured data
- community discovery
- dynamic networks
- graph theory
- graph structures
- small world
- complex networks
- clustering coefficient
- network structure
- social graphs
- user preferences
- graph theoretic
- item based collaborative filtering
- weighted graph
- directed graph
- heterogeneous networks
- recommendation systems
- graph model
- connected components
- network analysis
- protein interaction networks
- network size
- matrix factorization
- graph matching
- graph layout
- graph mining algorithms
- structural patterns
- social graph
- functional modules
- data sparsity
- path length
- biological networks
- graph representation
- graph mining
- directed acyclic graph