A Review on Deep Learning for Recommender Systems Challenges and Remedies

Abstruse

Recommender systems are constructive tools of information filtering that are prevalent due to increasing access to the Cyberspace, personalization trends, and changing habits of computer users. Although existing recommender systems are successful in producing decent recommendations, they withal endure from challenges such equally accuracy, scalability, and cold-start. In the concluding few years, deep learning, the state-of-the-fine 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 4 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 besides provide a comprehensive quantitative cess of publications in the field and conclude by discussing gained insights and possible future work on the subject.

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Affiliations

1. Computer Technology Department, Anadolu Academy, 26470, Eskisehir, Turkey

   Zeynep Batmaz, Ali Yurekli, Alper Bilge & Cihan Kaleli

Corresponding author

Correspondence to Alper Bilge.

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Cite this article

Batmaz, Z., Yurekli, A., Bilge, A. et al. A review on deep learning for recommender systems: challenges and remedies. Artif Intell Rev 52, 1–37 (2019). https://doi.org/10.1007/s10462-018-9654-y

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• Published: 29 Baronial 2018

• Issue Date: 01 June 2019

• DOI : https://doi.org/10.1007/s10462-018-9654-y

Keywords

• Recommender systems
• Deep learning
• Survey
• Accurateness
• Scalability
• Sparsity

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Source: https://link.springer.com/article/10.1007/s10462-018-9654-y

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