Factor Analysis and Model Studying for Improvement of Recommendation System 


Vol. 44,  No. 5, pp. 936-942, May  2019
10.7840/kics.2019.44.5.936


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  Abstract

The recommendation system is a personalized service that allows you to select the product that suits your taste. Its role is increasing in the internet age where mass information exists. The algorithm used to build the recommendation system predicts customer preference and MAE, which is the prediction accuracy of the algorithm"s preference, is important because it is related to the reliability of the recommendation system. This study analyzed whether specific customer information is related to MAE and analyzes its influence. The MAE of customer preference predicted by the neighborhood-based collaborative filtering algorithm of the recommendation system is analyzed, and classified as low and high MAE. To investigate whether the group is related to specific customer information and to analyze its influence, this binary logistic regression analysis was conducted to suggest a research model as a factor to improve the prediction accuracy of the recommendation system. In the proposed research model, the number of customer responses and occupation were analyzed as factors related to MAE.

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  Cite this article

[IEEE Style]

J. H. Koo and S. O. Kim, "Factor Analysis and Model Studying for Improvement of Recommendation System," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 5, pp. 936-942, 2019. DOI: 10.7840/kics.2019.44.5.936.

[ACM Style]

Jee Hyun Koo and Sun Ok Kim. 2019. Factor Analysis and Model Studying for Improvement of Recommendation System. The Journal of Korean Institute of Communications and Information Sciences, 44, 5, (2019), 936-942. DOI: 10.7840/kics.2019.44.5.936.

[KICS Style]

Jee Hyun Koo and Sun Ok Kim, "Factor Analysis and Model Studying for Improvement of Recommendation System," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 5, pp. 936-942, 5. 2019. (https://doi.org/10.7840/kics.2019.44.5.936)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880