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
  • Yonghong Yu
  • Qiang Wang
  • Li Zhang
  • Can Wang
  • Sifan Wu
  • Boyu Qi
  • Xiaotian Wu

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’ 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.
Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks (IJCNN)
Place of PublicationInternational Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
ISBN (Electronic)978-1-7281-1985-4
ISBN (Print)978-1-7281-1986-1
DOIs
Publication statusPublished - 30 Sep 2019
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 43383544