Deep Learning-Based Recommender Systems—A Systematic Review and Future Perspective
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-6706-3_33
Reference52 articles.
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2. Shahbazi, Z., Hazra, D., Park, S., Byun, Y.C.: Toward improving the prediction accuracy of product recommendation system using extreme gradient boosting and encoding approaches. Symmetry 12(9), 1566 (2020)
3. Lin, H., Huang, Y., Luo, Y.: The construction of learning resource recommendation system based on recognition technology. In: Smart Innovations in Communication and Computational Sciences, pp. 255–261. Springer, Singapore (2021)
4. Sreepada, R.S., Patra, B.K.: Enhancing long tail item recommendation in collaborative filtering: an econophysics-inspired approach. Electron. Commer. Res. Appl. 49 (2021), Article No. 101089
5. Chiu, M.C., Chen, T.C.T.: Assessing sustainable effectiveness of the adjustment mechanism of a ubiquitous clinic recommendation system. Health Care Manag. Sci. 23(2), 239–248 (2020)
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