Conditioned Variational Autoencoder for top-N item recommendation
This addresses constrained recommendation scenarios for users, but it is incremental as it builds on existing VAE methods.
The paper tackles the problem of top-N item recommendation under constraints by proposing a Conditioned Variational Autoencoder (C-VAE) that incorporates conditions into the model architecture and training loss, showing it generalizes a state-of-the-art model and provides accurate recommendations.
In this paper, we propose a Conditioned Variational Autoencoder (C-VAE) for constrained top-N item recommendation where the recommended items must satisfy a given condition. The proposed model architecture is similar to a standard VAE in which the condition vector is fed into the encoder. The constrained ranking is learned during training thanks to a new reconstruction loss that takes the input condition into account. We show that our model generalizes the state-of-the-art Mult-VAE collaborative filtering model. Moreover, we provide insights on what C-VAE learns in the latent space, providing a human-friendly interpretation. Experimental results underline the potential of C-VAE in providing accurate recommendations under constraints. Finally, the performed analyses suggest that C-VAE can be used in other recommendation scenarios, such as context-aware recommendation.