State-of-the-art recommender systems (RSs) generally try to improve the overall recommendation quality. However, users usually tend to explicitly filter the item set based on available categories, e.g., smartphone brands, movie genres. For this reason, an RS that can make this step automatically is likely to increase the user's experience. This paper proposes 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 a 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. Experimental results underline the potential of CVAE 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.

Conditioned Variational Autoencoder for Top-N Item Recommendation

Polato, M;
2022-01-01

Abstract

State-of-the-art recommender systems (RSs) generally try to improve the overall recommendation quality. However, users usually tend to explicitly filter the item set based on available categories, e.g., smartphone brands, movie genres. For this reason, an RS that can make this step automatically is likely to increase the user's experience. This paper proposes 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 a 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. Experimental results underline the potential of CVAE 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.
2022
International Conference on Artificial Neural Networks
Bristol, UK
6-9/9/22
Artificial Neural Networks and Machine Learning – ICANN 2022
SPRINGER INTERNATIONAL PUBLISHING AG
13530
785
796
978-3-031-15930-5
978-3-031-15931-2
Recommender systems; Collaborative filtering; Implicit feedback; Variational autoencoder; Top-N recommendation
Carraro, T; Polato, M; Bergamin, L; Aiolli, F
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1890155
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