Name
Abstract | ||
|---|---|---|
•The proposed MMR employs visual correlations of images that users consumed to reveal and infer users’ interests by interest propagation over the visual graph of images instead of propagating collaborative signals over users’ sparse interaction graph.•We constrain manifold learning within visual groups adaptively to propagate users’ interests and prevent bias propagated across semantics as a tradeoff between personality and propagation smoothness.•For image recommendation, the proposed MMR introduces manifold modularization to perform interest propagation in a decomposed manner and reduce computational burden exponentially. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2021.108100 | Pattern Recognition |
Keywords | DocType | Volume |
Manifold propagation,Modularization,Image recommendation,User interest | Journal | 120 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng Jian | 1 | 18 | 10.79 |
| Jingjing Guo | 2 | 0 | 0.34 |
| Chenlin Zhang | 3 | 0 | 0.68 |
| Ting Jia | 4 | 3 | 1.39 |
| Lifang Wu | 5 | 82 | 22.35 |
| Xun Yang | 6 | 148 | 14.26 |
| Lina Huo | 7 | 0 | 0.34 |