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Open AccessArticle

Attention Collaborative Autoencoder for Explicit Recommender Systems

by Shuo Chen and Min Wu *
School of Software Engineering, East China Normal University, Shanghai 200062, China
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(10), 1716; https://doi.org/10.3390/electronics9101716
Received: 31 August 2020 / Revised: 22 September 2020 / Accepted: 14 October 2020 / Published: 18 October 2020
(This article belongs to the Section Artificial Intelligence)
Recently, various deep learning-based models have been applied in the study of recommender systems. Some researches have combined the classic collaborative filtering method with deep learning frameworks in order to obtain more accurate recommendations. However, these models either add additional features, but still recommend in the original linear manner, or only extract the global latent factors of the rating matrices in a non-linear way without considering some local special relationships. In this paper, we propose a deep learning framework for explicit recommender systems, named Attention Collaborative Autoencoder (ACAE). Based on the denoising autoencoder, our model can extract the global latent factors in a non-linear fashion from the sparse rating matrices. In ACAE, attention units are introduced during back propagation, enabling discovering potential relationships between users and items in the neighborhood, which makes the model obtain better results in the rating prediction tasks. In addition, we propose how to optimize the training process of the model by proposing a new loss function. Experiments on two public datasets demonstrate the effectiveness of ACAE and its outperformance of competitive baselines. View Full-Text
Keywords: recommender systems; neural networks; collaborative filtering; deep learning recommender systems; neural networks; collaborative filtering; deep learning
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Chen, S.; Wu, M. Attention Collaborative Autoencoder for Explicit Recommender Systems. Electronics 2020, 9, 1716.

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