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A review on deep learning for recommender systems: challenges and remedies

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 subject.

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Notes

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    https://www.spotify.com/.

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    https://soundcloud.com/.

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    https://arxiv.org/.

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Correspondence to Alper Bilge.

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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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Keywords

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