Improving Social Recommendations with Item Relationships

Haifeng Liu; Hongfei Lin; Bo Xu; Liang Yang; Yuan Lin; Yonghe Chu; Wenqi Fan; Nan Zhao

doi:10.1007/978-3-030-63820-7_87

Improving Social Recommendations with Item Relationships

Haifeng Liu
, Hongfei Lin^*
, Bo Xu
, Liang Yang
, Yuan Lin
, Yonghe Chu
, Wenqi Fan
, Nan Zhao

^*Corresponding author for this work

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

Abstract

Social recommendations have witnessed rapid developments for improving the performance of recommender systems, due to the growing influence of social networks. However, existing social recommendations often ignore to facilitate the substitutable and complementary items to understand items and enhance the recommender systems. We propose a novel graph neural network framework to model the multi-graph data (user-item graph, user-user graph, item-item graph) in social recommendations. In particular, we introduce a viewpoint mechanism to model the relationship between users and items. We conduct an extensive experiment on two public benchmarks, demonstrating significant improvement over several state-of-the-art models.

Original language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	Proceedings, Part IV
Editors	Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
Publisher	Springer
Pages	763-770
ISBN (Electronic)	9783030638207
ISBN (Print)	9783030638191
DOIs	https://doi.org/10.1007/978-3-030-63820-7_87
Publication status	Published - 2020
Event	27th International Conference on Neural Information Processing (ICONIP 2020) - Virtual, Bangkok, Thailand Duration: 18 Nov 2020 → 22 Nov 2020 https://www.apnns.org/ICONIP2020/

Publication series

Name	Communications in Computer and Information Science
Volume	1332
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	27th International Conference on Neural Information Processing (ICONIP 2020)
Place	Thailand
City	Bangkok
Period	18/11/20 → 22/11/20
Internet address	https://www.apnns.org/ICONIP2020/

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Liu, H., Lin, H., Xu, B., Yang, L., Lin, Y., Chu, Y., Fan, W., & Zhao, N. (2020). Improving Social Recommendations with Item Relationships. In H. Yang, K. Pasupa, A. C.-S. Leung, J. T. Kwok, J. H. Chan, & I. King (Eds.), Neural Information Processing: Proceedings, Part IV (pp. 763-770). (Communications in Computer and Information Science; Vol. 1332). Springer . https://doi.org/10.1007/978-3-030-63820-7_87

@inproceedings{3b64c21445504c6cad15249a9b0e391a,

title = "Improving Social Recommendations with Item Relationships",

abstract = "Social recommendations have witnessed rapid developments for improving the performance of recommender systems, due to the growing influence of social networks. However, existing social recommendations often ignore to facilitate the substitutable and complementary items to understand items and enhance the recommender systems. We propose a novel graph neural network framework to model the multi-graph data (user-item graph, user-user graph, item-item graph) in social recommendations. In particular, we introduce a viewpoint mechanism to model the relationship between users and items. We conduct an extensive experiment on two public benchmarks, demonstrating significant improvement over several state-of-the-art models.",

author = "Haifeng Liu and Hongfei Lin and Bo Xu and Liang Yang and Yuan Lin and Yonghe Chu and Wenqi Fan and Nan Zhao",

year = "2020",

doi = "10.1007/978-3-030-63820-7\_87",

language = "English",

isbn = "9783030638191",

series = "Communications in Computer and Information Science",

publisher = "Springer ",

pages = "763--770",

editor = "Haiqin Yang and Kitsuchart Pasupa and Leung, \{Andrew Chi-Sing\} and Kwok, \{James T.\} and Chan, \{Jonathan H.\} and Irwin King",

booktitle = "Neural Information Processing",

note = "27th International Conference on Neural Information Processing (ICONIP 2020) ; Conference date: 18-11-2020 Through 22-11-2020",

url = "https://www.apnns.org/ICONIP2020/",

}

Liu, H, Lin, H, Xu, B, Yang, L, Lin, Y, Chu, Y, Fan, W & Zhao, N 2020, Improving Social Recommendations with Item Relationships. in H Yang, K Pasupa, AC-S Leung, JT Kwok, JH Chan & I King (eds), Neural Information Processing: Proceedings, Part IV. Communications in Computer and Information Science, vol. 1332, Springer , pp. 763-770, 27th International Conference on Neural Information Processing (ICONIP 2020), Bangkok, Thailand, 18/11/20. https://doi.org/10.1007/978-3-030-63820-7_87

Improving Social Recommendations with Item Relationships. / Liu, Haifeng; Lin, Hongfei; Xu, Bo et al.
Neural Information Processing: Proceedings, Part IV. ed. / Haiqin Yang; Kitsuchart Pasupa; Andrew Chi-Sing Leung; James T. Kwok; Jonathan H. Chan; Irwin King. Springer , 2020. p. 763-770 (Communications in Computer and Information Science; Vol. 1332).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Improving Social Recommendations with Item Relationships

AU - Liu, Haifeng

AU - Lin, Hongfei

AU - Xu, Bo

AU - Yang, Liang

AU - Lin, Yuan

AU - Chu, Yonghe

AU - Fan, Wenqi

AU - Zhao, Nan

PY - 2020

Y1 - 2020

N2 - Social recommendations have witnessed rapid developments for improving the performance of recommender systems, due to the growing influence of social networks. However, existing social recommendations often ignore to facilitate the substitutable and complementary items to understand items and enhance the recommender systems. We propose a novel graph neural network framework to model the multi-graph data (user-item graph, user-user graph, item-item graph) in social recommendations. In particular, we introduce a viewpoint mechanism to model the relationship between users and items. We conduct an extensive experiment on two public benchmarks, demonstrating significant improvement over several state-of-the-art models.

AB - Social recommendations have witnessed rapid developments for improving the performance of recommender systems, due to the growing influence of social networks. However, existing social recommendations often ignore to facilitate the substitutable and complementary items to understand items and enhance the recommender systems. We propose a novel graph neural network framework to model the multi-graph data (user-item graph, user-user graph, item-item graph) in social recommendations. In particular, we introduce a viewpoint mechanism to model the relationship between users and items. We conduct an extensive experiment on two public benchmarks, demonstrating significant improvement over several state-of-the-art models.

UR - https://www.scopus.com/pages/publications/85097300921

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85097300921&origin=recordpage

U2 - 10.1007/978-3-030-63820-7_87

DO - 10.1007/978-3-030-63820-7_87

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783030638191

T3 - Communications in Computer and Information Science

SP - 763

EP - 770

BT - Neural Information Processing

A2 - Yang, Haiqin

A2 - Pasupa, Kitsuchart

A2 - Leung, Andrew Chi-Sing

A2 - Kwok, James T.

A2 - Chan, Jonathan H.

A2 - King, Irwin

PB - Springer

T2 - 27th International Conference on Neural Information Processing (ICONIP 2020)

Y2 - 18 November 2020 through 22 November 2020

ER -

Improving Social Recommendations with Item Relationships

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