Abstract
We propose a novel hybrid recommender system method that treats missing data in
a principled manner and that uses amortized inference for fast predictions. We name
this method, the Partial Variational Autoencoder (p-VAE). P-VAE uses a novel
probabilistic generative model to handle varying numbers of user ratings in a principled way. Using the proposed amortized partial inference technique in p-VAEs,
learning and inference can be efficiently performed by minimizing the so-called
partial variational upper bound, without making ad-hoc assumptions on the values
of missing ratings. Empirical experiments on the MovieLens dataset demonstrate
the state-of-the-art performance of our method for movie recommendations.
a principled manner and that uses amortized inference for fast predictions. We name
this method, the Partial Variational Autoencoder (p-VAE). P-VAE uses a novel
probabilistic generative model to handle varying numbers of user ratings in a principled way. Using the proposed amortized partial inference technique in p-VAEs,
learning and inference can be efficiently performed by minimizing the so-called
partial variational upper bound, without making ad-hoc assumptions on the values
of missing ratings. Empirical experiments on the MovieLens dataset demonstrate
the state-of-the-art performance of our method for movie recommendations.
| Original language | English |
|---|---|
| Title of host publication | Bayesian Deep Learning |
| Subtitle of host publication | NIPS 2018 Workshop |
| Number of pages | 7 |
| State | Published - 2018 |
| Externally published | Yes |
| Event | NIPS 2018 Workshop: Bayesian Deep Learning - Palais des Congrès de Montréal, Montréal, Canada Duration: 7 Dec 2018 → 7 Dec 2018 http://bayesiandeeplearning.org/2018/ |
Workshop
| Workshop | NIPS 2018 Workshop |
|---|---|
| Abbreviated title | NIPS Workshop |
| Country/Territory | Canada |
| City | Montréal |
| Period | 7/12/18 → 7/12/18 |
| Internet address |