1. Deep learning-based collaborative filtering recommender systems: a comprehensive and systematic review.
- Author
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Torkashvand, Atena, Jameii, Seyed Mahdi, and Reza, Akram
- Subjects
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DEEP learning , *RECOMMENDER systems , *CONVOLUTIONAL neural networks , *INFORMATION overload - Abstract
Nowadays, the volume of online information is growing and it is difficult to find the required information. Effective strategies such as recommender systems are required to overcome information overload. Collaborative filtering is a widely used type of recommender system in e-commerce environments and can simply provide suggestions for users. Recently, deep learning approaches were applied in collaborative filtering to tackle some drawbacks. This systematic review aims to provide a comprehensive review of recent research efforts on deep learning-based collaborative filtering recommender systems. We explain the research methodology and paper selection process and the search query. 102 papers are selected out of 719 papers that were published between 2019 and May 2023. Furthermore, the approaches in the selected papers are classified into two main categories: memory-based and model-based techniques. The main ideas, advantages, disadvantages, used tools, type of neural network, applications, and evaluation parameters of each selected paper are also discussed in detail. It was found that CNN (Convolutional Neural Network), AE (Autoencoder), DNN (Deep neural network), and Hybrid networks are the four mostly used neural networks in recommender systems. Also, Python, MATLAB, and Java are the most frequently used tools in the reviewed papers. Regarding the applications of the recommender systems in the reviewed papers, movies, products, and music recommendation are three most frequent applications. We point out the open issues and future research directions. Some key challenges such as cold start, data sparsity, scalability, and accuracy are still open to be addressed. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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