Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network.
Chuhan WuFangzhao WuTao QiSuyu GeYongfeng HuangXing XiePublished in: EMNLP/IJCNLP (1) (2019)
Keyphrases
- user preferences
- item recommendation
- neural network
- recommender systems
- graph representation
- collaborative filtering
- graph representations
- recommendation algorithms
- graph structure
- user ratings
- long tail
- graph theory
- graph theoretic
- cold start problem
- directed graph
- user feedback
- graph databases
- graph matching
- recommendation systems
- graph model
- graph mining
- weighted graph
- matrix factorization
- adjacency matrix
- graph properties
- labeled graphs
- graph structures
- personalized recommendation
- graph construction
- graph classification
- bipartite graph
- active user
- tag recommendation
- series parallel
- graph search
- dynamic graph
- artificial neural networks
- graph data
- undirected graph
- random graphs
- cold start
- information overload
- graph theoretical
- collaborative recommendation
- graph partitioning
- user profiles
- social graph
- attributed graphs
- graph clustering
- knn
- personal preferences
- user similarity
- user interests
- planar graphs
- user model
- subgraph isomorphism
- query graph
- graph isomorphism
- pattern mining
- complex networks
- structured data
- product recommendation
- shortest path
- edge weights
- finding the shortest path