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arXiv · 2004.11141

Conditioned Variational Autoencoder for top-N item recommendation

Tommaso Carraro·Mirko Polato·Fabio Aiolli
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Abstract

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.

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Tommaso Carraro, Mirko Polato, Fabio Aiolli. 2020-04-16. Conditioned Variational Autoencoder for top-N item recommendation. https://arxiv.org/abs/2004.11141

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