Abstract
Recommender systems help users explore new content such as music and news by showing them what they will find potentially interesting. There are many methods and algorithms that can help recommender systems create personalized recommendations. All recommendation approaches can be divided into three categories: Content-based recommendation, Collaborative filtering and Hybrid methods. In this paper, we explore and compare Variational Autoencoders and Denoising Autoencoders for Collaborative Filtering with implicit feedback. A Variational Autoencoders(VAE) is a non-linear model, so it can capture patterns that are more complex in the data and since the forward pass is sufficient to obtain the recommendation of a given user then the query time is fast. A Denoising AutoEncoder is a specific type of AutoEncoder, which is generally classed as a type of deep neural network and is trained to use a hidden layer to reconstruct a particular model based on its inputs. Comparison results between Variational Autoencoders (VAE) and Denoising Autoencoders (DAE) show that VAE has the upper hand when it comes to large datasets while DAE is better when using small datasets. We explore and evaluate both methods on three public datasets and using different metrics.
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Bennouna, K., Chougrad, H., Khamlichi, Y., El Boushaki, A., El Haj Ben Ali, S. (2022). Variational Autoencoders Versus Denoising Autoencoders for Recommendations. In: Bennani, S., Lakhrissi, Y., Khaissidi, G., Mansouri, A., Khamlichi, Y. (eds) WITS 2020. Lecture Notes in Electrical Engineering, vol 745. Springer, Singapore. https://doi.org/10.1007/978-981-33-6893-4_17
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DOI: https://doi.org/10.1007/978-981-33-6893-4_17
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