Recommendation system based on deep learning methods: a systematic review and new directions

By Aminu Da’u, Naomie Binti Salim. Artificial Intelligence Review, 2019

368 citations recorded by the source corpus.

How researchers see this paper

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AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems (2022, methodology): “Techniques in this category are mostly employed for unsupervised learning, as they are commonly used for feature learning or data generation and representation [19, 20].”
A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “For example, Zhang et al. [16] organized the discussions on recommendation methods into MLP based, autoencoder based, RNN based, attention based, etc. Similar surveys can also be found [17], [18].”
Graph Neural Networks in Recommender Systems: A Survey (2020, background): “There exist surveys focusing on different perspectives of recommender systems [4, 16, 22, 28, 45, 117, 200].”
A survey on large language models for recommendation (2023, background): “Meanwhile, facing the common data sparsity issue of limited historical interactions [ Da’u and Salim, 2020 ] , LLMs also bring new possibilities to recommendation systems through zero/few-shot recommendation capabilities [ Sileo et al. , 2022 ] .”
Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions (2021, methodology): “As a result, the methods under this category are essentially applied for unsupervised learning as the methods are typically used for feature learning or data generating and representation [21]”