Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 186, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2021.115825
Keywords
Autoencoder; Personalized recommendation; Semi-autoencoder; Representation learning; Collaborative filtering
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Funding
- National Natural Science Foundation of China [61906060]
- National Key Research and Development Program of China [2016YFC0801406]
- Program for Changjiang Scholas and Innovative Research Team in University (PCSIRT) of the Ministry of Education of China [IRT17R32]
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The CAPR method uses collaborative autoencoder for personalized recommendation, learning feature representations of users and items to address different characteristics and sparsity issues. Experimental results demonstrate the effectiveness of this method compared to others.
In the past decades, recommendation systems have provided lots of valuable personalized suggestions for the users to address the problem of information over-loaded. Collaborative Filtering (CF) is one of the most commonly applied and successful recommendation approaches, which refers to using the preferences of groups with similar interests to recommend information to other users. Recently, in addition to the traditional matrix factorization techniques, deep learning methods have been proposed to learn more abstract and higher-level representations for recommendation. However, most previous deep recommendation methods learn the higher level feature representations of users and items through an identical model structure, which ignores the different characteristics of the user-based and item-based data. In addition, the rating matrix is usually sparse which may result in a significant degradation of recommendation performance. To address these problems, we propose a representation learning method with Collaborative Autoencoder for Personalized Recommendation (CAPR for short). In this method, user-based and item-based feature representations are learned by two different autoencoders for capturing different features of the data. Meanwhile, items' attributions are combined into the feature representations with semi-autoencoder for alleviating the sparsity problem. Extensive experimental results confirm the effectiveness of our proposed method compared to other state-of-the-art matrix factorization methods and deep recommendation methods.
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