Journal ArticleDOI
A review on deep learning for recommender systems: challenges and remedies
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TLDR
This study provides a comprehensive review of deep learning-based recommendation approaches to enlighten and guide newbie researchers interested in the subject.Abstract:
Recommender systems are effective tools of information filtering that are prevalent due to increasing access to the Internet, personalization trends, and changing habits of computer users Although existing recommender systems are successful in producing decent recommendations, they still suffer from challenges such as accuracy, scalability, and cold-start In the last few years, deep learning, the state-of-the-art machine learning technique utilized in many complex tasks, has been employed in recommender systems to improve the quality of recommendations In this study, we provide a comprehensive review of deep learning-based recommendation approaches to enlighten and guide newbie researchers interested in the subject We analyze compiled studies within four dimensions which are deep learning models utilized in recommender systems, remedies for the challenges of recommender systems, awareness and prevalence over recommendation domains, and the purposive properties We also provide a comprehensive quantitative assessment of publications in the field and conclude by discussing gained insights and possible future work on the subjectread more
Citations
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Journal ArticleDOI
A survey of the recent architectures of deep convolutional neural networks
TL;DR: Deep Convolutional Neural Networks (CNNs) as mentioned in this paper are a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing.
Journal ArticleDOI
A Survey on Deep Learning for Named Entity Recognition
TL;DR: A comprehensive review on existing deep learning techniques for NER is provided in this paper, where the authors systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder.
Posted Content
A Survey on Deep Learning for Named Entity Recognition
TL;DR: A comprehensive review on existing deep learning techniques for NER, including tagged NER corpora and off-the-shelf NER tools, and systematically categorizes existing works based on a taxonomy along three axes.
Posted Content
Graph Neural Networks in Recommender Systems: A Survey
TL;DR: This article provides a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks and systematically analyze the challenges of applying GNN on different types of data.
Journal ArticleDOI
A Survey on Deep Learning for Named Entity Recognition
TL;DR: A comprehensive review on existing deep learning techniques for NER can be found in this article , where the authors systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder.
References
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Proceedings ArticleDOI
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TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.