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%.
- Recommendation system
- Sparse trust
- Transductive Support Vector Machine
Hu, Z., Xu, G., Zheng, X., Liu, J., Li, Z., Sheng, Q. Z., ... Xian, H. (2020). SSL-SVD: semi-supervised learning based sparse trust recommendation. ACM Transactions on Internet Technology, 20(1), . https://doi.org/10.1145/3369390