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

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


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
Title of host publication2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781728109626
Publication statusPublished - 2019
Event2019 IEEE Global Communications Conference - Waikoloa, United States
Duration: 9 Dec 201913 Dec 2019


Conference2019 IEEE Global Communications Conference
Abbreviated titleIEEE GLOBECOM 2019
Country/TerritoryUnited States


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


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