SSL-STR: Semi-supervised learning for sparse trust recommendation

Zhengdi Hu, Guangquan Xu, Xi Zheng, Jiang Liu, Xiaojiang Du

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Trust is widely applied in recommender systems to improve recommendation performance by alleviating well-known problems, such as cold start, data sparsity, and so on. However, trust data itself also faces sparse problems. To solve these problems, we propose a novel sparse trust recommendation model, SSL-STR. Specifically, we decompose the aspects influencing trust-building into finer-grained factors, and combine these factors to mine the implicit sparse trust relationships among users by employing the Transductive Support Vector Machine algorithm. Then we extend SVD++ model with social trust and sparse trust information for rating prediction in the recommendation system. Experiments show that our SSL-STR improves the recommendation accuracy by up to 4.3%.

Original languageEnglish
Article number9014085
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
DOIs
StatePublished - 2019
Event2019 IEEE Global Communications Conference, GLOBECOM 2019 - Waikoloa, United States
Duration: 9 Dec 201913 Dec 2019

Keywords

  • Recommendation system
  • Sparse trust
  • SSL-STR
  • SVD++
  • Transductive Support Vector Machine

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