Name
Abstract | ||
|---|---|---|
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%.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3369390 | ACM Transactions on Internet Technology (TOIT) |
Keywords | Field | DocType |
SSL-SVD,SVD++,Sparse trust,Transductive Support Vector Machine,recommendation system | Data mining,Singular value decomposition,Semi-supervised learning,Computer science | Journal |
Volume | Issue | ISSN |
20 | 1 | 1533-5399 |
Citations | PageRank | References |
1 | 0.35 | 31 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhengdi Hu | 1 | 1 | 0.35 |
| Guangquan Xu | 2 | 171 | 33.20 |
| Xi Zheng | 3 | 154 | 24.34 |
| Jiang Liu | 4 | 1 | 0.69 |
| Zhangbing Li | 5 | 1 | 0.35 |
| Quan Z. Sheng | 6 | 3520 | 301.77 |
| Wenjuan Lian | 7 | 9 | 1.77 |
| Hequn Xian | 8 | 12 | 4.87 |