SSL-SVD: semi-supervised learning based sparse trust recommendation

Zhengdi Hu, Guangquan Xu*, Xi Zheng, Jiang Liu, Zhangbing Li, Quan Z. Sheng, Wenjuan Lian, Hequn Xian

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


Recommendation systems have been widely used in large e-commerce websites, but cold start and data sparsity seriously affect the accuracy of recommendation. To solve these problems, we propose SSL-SVD, which works to mine the sparse trust between users and improve the performance of the recommendation system. Specifically, we mine sparse trust relationships by decomposing trust impact into fine-grained factors and employing the Transductive Support Vector Machine algorithm to combine these factors. Then, we incorporate both social trust and sparse trust information into the SVD++ model, which can effectively utilize the explicit and implicit influence of trust for rating prediction in the recommendation system. Experiments show that our SSL-SVD increases the trust density degree of each dataset by more than 65% and improves the recommendation accuracy by up to 4.3%.
Original languageEnglish
Article number4
Pages (from-to)1-20
Number of pages20
JournalACM Transactions on Internet Technology
Issue number1
Publication statusPublished - Mar 2020


  • Recommendation system
  • Sparse trust
  • SVD++
  • Transductive Support Vector Machine


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