International Journal of Computational Intelligence Systems

Volume 14, Issue 1, 2021, Pages 1303 - 1314

H2Rec: Homogeneous and Heterogeneous Network Embedding Fusion for Social Recommendation

Authors
Yabin Shao*, ORCID, Cheng Liu
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Nan’an District of Chongqing, Chongqing, 400065, China
*Corresponding author. Email: shaoyb@cqupt.edu.cn
Corresponding Author
Yabin Shao
Received 7 December 2020, Accepted 30 March 2021, Available Online 13 April 2021.
DOI
https://doi.org/10.2991/ijcis.d.210406.001How to use a DOI?
Keywords
Homogeneous information network; Heterogeneous information network; Network embedding; Social recommendation
Abstract

Due to the problems of data sparsity and cold start in traditional recommendation systems, social information is introduced. From the perspective of heterogeneity, it reflects the indirect relationship between users, and from the perspective of homogeneity, it reflects the direct relationship between users. At present, most social recommendation is based on the homogeneity or heterogeneity of social networks. Few studies consider both of them at the same time, and the deep structure of social networks is not extensively exploited and comprehensively explore. To address these issues, we propose a unified H2Rec model to fuse homogeneous and heterogeneous information for recommendations in social networks. Considering the rich semantics reflected by metapaths in heterogeneous information and the wealth of social information reflected by homogeneous information, the proposed method uses a random walk strategy to generate node sequences in a homogeneous information network and a random walk strategy guided by metapaths to generate node sequences in a heterogeneous information network (HIN). Finally, we combine the two parts into a unified model for social recommendation. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
14 - 1
Pages
1303 - 1314
Publication Date
2021/04/13
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.d.210406.001How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Yabin Shao
AU  - Cheng Liu
PY  - 2021
DA  - 2021/04/13
TI  - H2Rec: Homogeneous and Heterogeneous Network Embedding Fusion for Social Recommendation
JO  - International Journal of Computational Intelligence Systems
SP  - 1303
EP  - 1314
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210406.001
DO  - https://doi.org/10.2991/ijcis.d.210406.001
ID  - Shao2021
ER  -