JoVA-hinge: joint variational autoencoders for personalized recommendation with implicit feedback
Askari Firoozjayi, Bahare
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Recently, Variational Autoencoders (VAEs) have shown remarkable performance in collaborative filtering (CF) with implicit feedback. These existing recommendation models learn user representations to reconstruct or predict user preferences. However, existing VAE-based recommendation models learn user and item representations separately. This thesis introduces joint variational autoencoders (JoVA). JoVA, as an ensemble of two VAEs, simultaneously and jointly learns both user-user and item-item correlations and collectively reconstructs and predicts user preferences. Moreover, a variant of JoVA, referred to as JoVA-Hinge, is introduced to improve recommendation quality. JoVA-Hinge incorporates pairwise ranking loss to VAE's losses. Extensive experiments on multiple real-world datasets show that our model can outperform state-of-the-art under a variety of commonly-used metrics. Our empirical experiments also confirm that JoVA-Hinge offers better results than existing methods for cold-start users with limited training data.