Feature Combination Hybrid System : Content-Base Combined With Matrix Factorizations 


Vol. 45,  No. 1, pp. 165-179, Jan.  2020
10.7840/kics.2020.45.1.165


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  Abstract

In recommendation system, all basis models seem rather restrictive in isolation, especially when various sources of data are available. Hybrid recommender systems have been designed to explore these possibilities. Conventional research in hybrid system has been limited to the development of new algorithms and little research combines the different approaches. Moreover, data stream and evaluating incremental model are necessary in big data era. In this research, we propose a feature combination hybrid system based on conjunction content-base and Matrix Factorization. In addition, we also show a prequential evaluation protocol for recommender systems, applicable for streaming data environments. Comparing with other state-of-the-art models, our algorithm gains better results in both accuracy and time update.

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  Cite this article

[IEEE Style]

S. Nguyen, H. Rou, E. Choi, G. Gim, "Feature Combination Hybrid System : Content-Base Combined With Matrix Factorizations," The Journal of Korean Institute of Communications and Information Sciences, vol. 45, no. 1, pp. 165-179, 2020. DOI: 10.7840/kics.2020.45.1.165.

[ACM Style]

Si-Thin Nguyen, Ho-Gun Rou, Eun-Jin Choi, and Gwang-Yong Gim. 2020. Feature Combination Hybrid System : Content-Base Combined With Matrix Factorizations. The Journal of Korean Institute of Communications and Information Sciences, 45, 1, (2020), 165-179. DOI: 10.7840/kics.2020.45.1.165.

[KICS Style]

Si-Thin Nguyen, Ho-Gun Rou, Eun-Jin Choi, Gwang-Yong Gim, "Feature Combination Hybrid System : Content-Base Combined With Matrix Factorizations," The Journal of Korean Institute of Communications and Information Sciences, vol. 45, no. 1, pp. 165-179, 1. 2020. (https://doi.org/10.7840/kics.2020.45.1.165)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880