Name
Abstract | ||
|---|---|---|
In recent years, Variational Autoencoders (VAEs) have been shown to be highly effective in both standard collaborative filtering applications and extensions such as incorporation of implicit feedback. We extend VAEs to collaborative filtering with side information, for instance when ratings are combined with explicit text feedback from the user. Instead of using a user-agnostic standard Gaussian prior, we incorporate user-dependent priors in the latent VAE space to encode users' preferences as functions of the review text. Taking into account both the rating and the text information to represent users in this multimodal latent space is promising to improve recommendation quality. Our proposed model is shown to outperform the existing VAE models for collaborative filtering (up to 29.41% relative improvement in ranking metric) along with other baselines that incorporate both user ratings and text for item recommendation. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3270323.3270329 | PROCEEDINGS OF THE 3RD WORKSHOP ON DEEP LEARNING FOR RECOMMENDER SYSTEMS (DLRS) |
Keywords | DocType | Volume |
Item Recommendation, Variational Autoencoders, Deep Learning, Probabilistic Modeling, Text Mining | Conference | abs/1807.06651 |
Citations | PageRank | References |
0 | 0.34 | 29 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giannis Karamanolakis | 1 | 5 | 3.46 |
| Kevin Raji Cherian | 2 | 0 | 0.34 |
| Ananth Ravi Narayan | 3 | 0 | 0.34 |
| Jie Yuan | 4 | 9 | 4.26 |
| Da Tang | 5 | 2 | 1.04 |
| Tony Jebara | 6 | 2078 | 196.32 |