TY - JOUR
T1 - HeteGraph
T2 - graph learning in recommender systems via graph convolutional networks
AU - Tran, Dai Hoang
AU - Sheng, Quan Z.
AU - Zhang, Wei Emma
AU - Aljubairy, Abdulwahab
AU - Zaib, Munazza
AU - Hamad, Salma Abdalla
AU - Tran, Nguyen H.
AU - Khoa, Nguyen Lu Dang
PY - 2021/1/8
Y1 - 2021/1/8
N2 - With the explosive growth of online information, many recommendation methods have been proposed. This research direction is boosted with deep learning architectures, especially the recently proposed graph convolutional networks (GCNs). GCNs have shown tremendous potential in graph embedding learning thanks to its inductive inference property. However, most of the existing GCN-based methods focus on solving tasks in the homogeneous graph settings, and none of them considers heterogeneous graph settings. In this paper, we bridge the gap by developing a novel framework called HeteGraph based on the GCN principles. HeteGraph can handle heterogeneous graphs in the recommender systems. Specifically, we propose a sampling technique and a graph convolutional operation to learn high-quality graph’s node embeddings, which differs from the traditional GCN approaches where a full graph adjacency matrix is needed for the embedding learning. We design two models based on the HeteGraph framework to evaluate two important recommendation tasks, namely item rating prediction and diversified item recommendations. Extensive experiments show the encouraging performance of HeteGraph on the first task and the state-of-the-art performance on the second task.
AB - With the explosive growth of online information, many recommendation methods have been proposed. This research direction is boosted with deep learning architectures, especially the recently proposed graph convolutional networks (GCNs). GCNs have shown tremendous potential in graph embedding learning thanks to its inductive inference property. However, most of the existing GCN-based methods focus on solving tasks in the homogeneous graph settings, and none of them considers heterogeneous graph settings. In this paper, we bridge the gap by developing a novel framework called HeteGraph based on the GCN principles. HeteGraph can handle heterogeneous graphs in the recommender systems. Specifically, we propose a sampling technique and a graph convolutional operation to learn high-quality graph’s node embeddings, which differs from the traditional GCN approaches where a full graph adjacency matrix is needed for the embedding learning. We design two models based on the HeteGraph framework to evaluate two important recommendation tasks, namely item rating prediction and diversified item recommendations. Extensive experiments show the encouraging performance of HeteGraph on the first task and the state-of-the-art performance on the second task.
KW - Graph convolutional network
KW - Heterogeneous graphs
KW - Neural networks
KW - Recommender systems
UR - http://www.scopus.com/inward/record.url?scp=85099232022&partnerID=8YFLogxK
U2 - 10.1007/s00521-020-05667-z
DO - 10.1007/s00521-020-05667-z
M3 - Article
AN - SCOPUS:85099232022
JO - Neural Computing and Applications
JF - Neural Computing and Applications
SN - 0941-0643
ER -