Network Representation Learning Enhanced Recommendation Algorithm. / Wang, Qiang ; Yu, Yonghong; Gao, Haiyan ; Zhang, Li; Cao, Yang; Mao, Lin; Dou, Kaiqi; Ni, Wenye .

In: IEEE Access, Vol. 7, 10.05.2019, p. 61388-61399.

Research output: Contribution to journalArticlepeer-review

  • Qiang Wang
  • Yonghong Yu
  • Haiyan Gao
  • Li Zhang
  • Yang Cao
  • Lin Mao
  • Kaiqi Dou
  • Wenye Ni


With the popularity of social network applications, more and more recommender systems utilize trust relationships to improve the performance of traditional recommendation algorithms. Social-network-based recommendation algorithms generally assume that users with trust relations usually share common interests. However, the performance of most of the existing social-network-based recommendation algorithms is limited by the coarse-grained and sparse trust relationships. In this paper, we propose a network representation learning enhanced recommendation algorithm. Specifically, we first adopt a network representation technique to embed social network into a low-dimensional space, and then utilize the low-dimensional representations of users to infer fine-grained and dense trust relationships between users. Finally, we integrate the fine-grained and dense trust relationships into the matrix factorization model to learn user and item latent feature vectors. The experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based recommendation algorithms.
Original languageEnglish
Pages (from-to)61388-61399
Number of pages12
JournalIEEE Access
Publication statusPublished - 10 May 2019
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

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