Integrating Social Circles and Network Representation Learning for Item Recommendation. / Yu, Yonghong; Wang, Qiang ; Zhang, Li; Wang, Can; Wu, Sifan ; Qi, Boyu ; Wu, Xiaotian .

2019 International Joint Conference on Neural Networks (IJCNN). International Joint Conference on Neural Networks (IJCNN) : IEEE, 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published

Standard

Integrating Social Circles and Network Representation Learning for Item Recommendation. / Yu, Yonghong; Wang, Qiang ; Zhang, Li; Wang, Can; Wu, Sifan ; Qi, Boyu ; Wu, Xiaotian .

2019 International Joint Conference on Neural Networks (IJCNN). International Joint Conference on Neural Networks (IJCNN) : IEEE, 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Yu, Y, Wang, Q, Zhang, L, Wang, C, Wu, S, Qi, B & Wu, X 2019, Integrating Social Circles and Network Representation Learning for Item Recommendation. in 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/IJCNN.2019.8852217

APA

Yu, Y., Wang, Q., Zhang, L., Wang, C., Wu, S., Qi, B., & Wu, X. (2019). Integrating Social Circles and Network Representation Learning for Item Recommendation. In 2019 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/IJCNN.2019.8852217

Vancouver

Yu Y, Wang Q, Zhang L, Wang C, Wu S, Qi B et al. Integrating Social Circles and Network Representation Learning for Item Recommendation. In 2019 International Joint Conference on Neural Networks (IJCNN). International Joint Conference on Neural Networks (IJCNN): IEEE. 2019 https://doi.org/10.1109/IJCNN.2019.8852217

Author

Yu, Yonghong ; Wang, Qiang ; Zhang, Li ; Wang, Can ; Wu, Sifan ; Qi, Boyu ; Wu, Xiaotian . / Integrating Social Circles and Network Representation Learning for Item Recommendation. 2019 International Joint Conference on Neural Networks (IJCNN). International Joint Conference on Neural Networks (IJCNN) : IEEE, 2019.

BibTeX

@inproceedings{c21ab4bbe6854729941e6201453f0914,
title = "Integrating Social Circles and Network Representation Learning for Item Recommendation",
abstract = "With the increasing popularity of social network services, social network platforms provide rich and additional information for recommendation algorithms. More and more researchers utilize trust relationships of users to improve the performance of recommendation algorithms. However, most of existing social-network-based recommendation algorithms ignore the following problems: (1) In different domains, users tend to trust different friends. (2) the performance of recommendation algorithms is limited by the coarse-grained trust relationships. In this paper, we propose a novel recommendation algorithm that integrates social circles and network representation learning for item recommendation. Specifically, we first infer domain-specific social trust circles based on original users{\textquoteright} rating information and social network information. Next, we adopt network representation technique to embed domain-specific social trust circle into a low-dimensional space, and then utilize the low-dimensional representations of users to infer the fine-grained trust relationships between users. Finally, we integrate the fine-gained trust relationships into domain-specific matrix factorization model to learn latent user and item feature vectors. Experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based recommendation algorithms.",
author = "Yonghong Yu and Qiang Wang and Li Zhang and Can Wang and Sifan Wu and Boyu Qi and Xiaotian Wu",
year = "2019",
month = sep,
day = "30",
doi = "10.1109/IJCNN.2019.8852217",
language = "English",
isbn = "978-1-7281-1986-1",
booktitle = "2019 International Joint Conference on Neural Networks (IJCNN)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Integrating Social Circles and Network Representation Learning for Item Recommendation

AU - Yu, Yonghong

AU - Wang, Qiang

AU - Zhang, Li

AU - Wang, Can

AU - Wu, Sifan

AU - Qi, Boyu

AU - Wu, Xiaotian

PY - 2019/9/30

Y1 - 2019/9/30

N2 - With the increasing popularity of social network services, social network platforms provide rich and additional information for recommendation algorithms. More and more researchers utilize trust relationships of users to improve the performance of recommendation algorithms. However, most of existing social-network-based recommendation algorithms ignore the following problems: (1) In different domains, users tend to trust different friends. (2) the performance of recommendation algorithms is limited by the coarse-grained trust relationships. In this paper, we propose a novel recommendation algorithm that integrates social circles and network representation learning for item recommendation. Specifically, we first infer domain-specific social trust circles based on original users’ rating information and social network information. Next, we adopt network representation technique to embed domain-specific social trust circle into a low-dimensional space, and then utilize the low-dimensional representations of users to infer the fine-grained trust relationships between users. Finally, we integrate the fine-gained trust relationships into domain-specific matrix factorization model to learn latent user and item feature vectors. Experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based recommendation algorithms.

AB - With the increasing popularity of social network services, social network platforms provide rich and additional information for recommendation algorithms. More and more researchers utilize trust relationships of users to improve the performance of recommendation algorithms. However, most of existing social-network-based recommendation algorithms ignore the following problems: (1) In different domains, users tend to trust different friends. (2) the performance of recommendation algorithms is limited by the coarse-grained trust relationships. In this paper, we propose a novel recommendation algorithm that integrates social circles and network representation learning for item recommendation. Specifically, we first infer domain-specific social trust circles based on original users’ rating information and social network information. Next, we adopt network representation technique to embed domain-specific social trust circle into a low-dimensional space, and then utilize the low-dimensional representations of users to infer the fine-grained trust relationships between users. Finally, we integrate the fine-gained trust relationships into domain-specific matrix factorization model to learn latent user and item feature vectors. Experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based recommendation algorithms.

U2 - 10.1109/IJCNN.2019.8852217

DO - 10.1109/IJCNN.2019.8852217

M3 - Conference contribution

SN - 978-1-7281-1986-1

BT - 2019 International Joint Conference on Neural Networks (IJCNN)

PB - IEEE

CY - International Joint Conference on Neural Networks (IJCNN)

ER -