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Variational Autoencoders (VAEs) have recently shown promising performance incollaborative filtering with implicit feedback. These existing recommendationmodels learn user representations to reconstruct or predict user preferences.We introduce joint variational autoencoders (JoVA), an ensemble of two VAEs, inwhich VAEs jointly learn both user and item representations and collectivelyreconstruct and predict user preferences. This design allows JoVA to captureuser-user and item-item correlations simultaneously. By extending the objectivefunction of JoVA with a hinge-based pairwise loss function (JoVA-Hinge), wefurther specialize it for top-k recommendation with implicit feedback. Ourextensive experiments on several real-world datasets show that JoVA-Hingeoutperforms a broad set of state-of-the-art collaborative filtering methods,under a variety of commonly-used metrics. Our empirical results also confirmthe outperformance of JoVA-Hinge over existing methods for cold-start userswith a limited number of training data.

Joint Variational Autoencoders for Recommendation with Implicit Feedback