A Hybrid Conditional Variational Autoencoder Model for Personalised Top-n Recommendation

The interactions of users with a recommendation system are in general sparse, leading to the well-known cold-start problem. Side information, such as age, occupation, genre and category, have been widely used to learn latent representations for users and items in order to address the sparsity of users' interactions. Conditional Variational Autoencoders (CVAEs) have recently been adapted for integrating side information as conditions to constrain the learned latent factors and to thereby generate personalised recommendations. However, the learning of effective latent representations that encapsulate both user (e.g. demographic information) and item side information (e.g. item categories) is still challenging. In this paper, we propose a new recommendation model, called Hybrid Conditional Variational Autoencoder (HCVAE) model, for personalised top-n recommendation, which effectively integrates both user and item side information to tackle the cold-start problem. Two CVAE-based methods -- using conditions on the learned latent factors, or conditions on the encoders and decoders -- are compared for integrating side information as conditions. Our HCVAE model leverages user and item side information as part of the optimisation objective to help the model construct more expressive latent representations and to better capture attributes of the users and items (such as demographic, category preferences) within the personalised item probability distributions. Thorough and extensive experiments conducted on both the MovieLens and Ta-feng datasets demonstrate that the HCVAE model conditioned on user category preferences with conditions on the learned latent factors can significantly outperform common existing top-n recommendation approaches such as MF-based and VAE/CVAE-based models.