Augmented Variational Autoencoders for Collaborative Filtering with Auxiliary Information

By Wonsung Lee, Kyungwoo Song, Il-Chul Moon. 2017

70 citations recorded by the source corpus.

Recommender systems offer critical services in the age of mass information. A good recommender system selects a certain item for a specific user by recognizing why the user might like the item. This awareness implies that the system should model the background of the items and the users. This background modeling for recommendation is tackled through the various models of collaborative filtering with auxiliary information. This paper presents variational approaches for collaborative filtering to deal with auxiliary information. The proposed methods encompass variational autoencoders through augmenting structures to model the auxiliary information and to model the implicit user feedback. This augmentation includes the ladder network and the generative adversarial network to extract the low-dimensional representations influenced by the auxiliary information. These two augmentations are the first trial in the venue of the variational autoencoders, and we demonstrate their significant improvement on the performances in the applications of the collaborative filtering.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

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Citations in the corpus

BooVAE: Boosting Approach for Continual Learning of VAE (2019, background): “Variational Autoencoders (VAEs) (Kingma & Welling, 2013) are deep generative models used in various domains (Lee et al., 2017; Zhou et al., 2020).”