Name
Abstract | ||
|---|---|---|
Although it is one of the most widely used methods in recommender systems, Collaborative Filtering (CF) still has difficulties in modeling non-linear user–item interactions. Complementary to this, recently developed deep generative model variants (e.g., Variational Autoencoder (VAE)) allowing Bayesian inference and approximation of the variational posterior distributions in these models, have achieved promising performance improvement in many areas. However, the choices of variation distribution – e.g., the popular diagonal-covariance Gaussians – are insufficient to recover the true distributions, often resulting in biased maximum likelihood estimates of the model parameters. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neunet.2020.03.010 | Neural Networks |
Keywords | DocType | Volume |
Collaborative recommendation,Variational inference,Normalizing flows,Autoregressive flows,Generative models | Journal | 126 |
Issue | ISSN | Citations |
1 | 0893-6080 | 1 |
PageRank | References | Authors |
0.35 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Zhou | 1 | 39 | 14.05 |
| Yuhua Mo | 2 | 1 | 0.35 |
| Goce Trajcevski | 3 | 1732 | 141.26 |
| Kunpeng Zhang | 4 | 156 | 26.02 |
| Jin Wu | 5 | 1 | 0.35 |
| Ting Zhong | 6 | 14 | 2.21 |