@inproceedings{7889a8db75bc4aada2ea54e341d67ecb,
title = "HeteGraph: a convolutional framework for graph learning in recommender systems",
abstract = "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. For evaluation, 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 our HeteGraph's encouraging performance on the first task and state-of-the-art performance on the second task.",
keywords = "Heterogeneous Graph, Recommender System, Graph Convolutional Network",
author = "Tran, {Dai Hoang} and Abdulwahab Aljubairy and Munazza Zaib and Sheng, {Quan Z.} and Zhang, {Wei Emma} and Tran, {Nguyen H.} and Nguyen, {Khoa L. D.}",
year = "2020",
doi = "10.1109/IJCNN48605.2020.9207078",
language = "English",
series = "IEEE International Joint Conference on Neural Networks (IJCNN)",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "1--8",
booktitle = "2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings",
address = "United States",
note = "2020 International Joint Conference on Neural Networks, IJCNN 2020 ; Conference date: 19-07-2020 Through 24-07-2020",
}