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http://dx.doi.org/10.7236/JIIBC.2021.21.3.67

Granule-based Association Rule Mining for Big Data Recommendation System  

Park, In-Kyu (Dept.of GameSoftware Eng., Joongbu University)
Publication Information
The Journal of the Institute of Internet, Broadcasting and Communication / v.21, no.3, 2021 , pp. 67-72 More about this Journal
Abstract
Association rule mining is a method of showing the relationship between patterns hidden in several tables. These days, granulation logic is used to add more detailed meaning to association rule mining. In addition, unlike the existing system that recommends using existing data, the granulation related rules can also recommend new subscribers or new products. Therefore, determining the qualitative size of the granulation of the association rule determines the performance of the recommendation system. In this paper, we propose a granulation method for subscribers and movie data using fuzzy logic and Shannon entropy concepts in order to understand the relationship to the movie evaluated by the viewers. The research is composed of two stages: 1) Identifying the size of granulation of data, which plays a decisive role in the implications of the association rules between viewers and movies; 2) Mining the association rules between viewers and movies using these granulations. We preprocessed Netflix's MovieLens data. The results of meanings of association rules and accuracy of recommendation are suggested with managerial implications in conclusion section.
Keywords
Recommendation system; Association rule mining; Fuzzy logic; Shannon entropy; Granules;
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