Employment of Machine Learning and Data Mining Technique in Hybrid Recommender System: A Comprehensive Survey
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-0098-3_49
Reference20 articles.
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3. Chen R, Hua Q, Chang Y-S, Wang B, Zhang L, Kong X (2018) A survey of collaborative filtering-based recommender systems: from traditional methods to hybrid methods based on social networks. IEEE Access, pp 64301–64320
4. Lops P, de Gemmis M, Semeraro G (2011) Content-based recommender systems: state of the art and trends. In: Ricci F et al. (eds) Recommender systems handbook, Springer
5. Kumar B, Sharma N (2016) Approaches, ıssues and challenges in recommender systems: a systematic review. In Indian J Sci Technol 9(47). https://doi.org/10.17485/ijst/2016/v9i47/9489
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