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Deep learning techniques for rating prediction: a survey of the state-of-the-art

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Abstract

With the growth of online information, varying personalization drifts and volatile behaviors of internet users, recommender systems are effective tools for information filtering to overcome the information overload problem. Recommender systems utilize rating prediction approaches i.e. predicting the rating that a user will give to a particular item, to generate ranked lists of items according to the preferences of each user in order to make personalized recommendations. Although previous recommendation systems are effective in creating attired recommendations, however, they still suffer from different types of challenges such as accuracy, scalability, cold-start, and data sparsity. In the last few years, deep learning has attained substantial interest in various research areas such as computer vision, speech recognition, and natural language processing. Deep learning based approaches are vigorous in not only performance improvement but also to feature representations learning from the scratch. The impact of deep learning is also prevalent, recently validating its efficacy on information retrieval and recommender systems research. In this study, a comprehensive review of deep learning-based rating prediction approaches is provided to help out new researchers interested in the subject. More concretely, the classification of deep learning-based recommendation/rating prediction models is provided and articulated along with an extensive summary of the state-of-the-art. Lastly, new trends are exposited with new perspectives pertaining to this novel and exciting development of the field.

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Notes

  1. https://pathmind.com/wiki/restricted-boltzmann-machine.

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Acknowledgements

This work was supported by the National Key R&D Program of China (No. 2019YFB1406302), National Natural Science Foundation of China (No. 61370137), the National Basic Research Program of China (No.2012CB7207002), the Ministry of Education—China Mobile Research Foundation Project (2016/2-7).

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  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Zahid Younas Khan, Zhendong Niu, Sulis Sandiwarno & Rukundo Prince

  2. Department of Computer Science and Information Technology, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan

    Zahid Younas Khan

  3. School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, USA

    Zhendong Niu

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Khan, Z.Y., Niu, Z., Sandiwarno, S. et al. Deep learning techniques for rating prediction: a survey of the state-of-the-art. Artif Intell Rev 54, 95–135 (2021). https://doi.org/10.1007/s10462-020-09892-9

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