The glut of information aggravated the process of data analysis and other procedures including data mining. Many algorithms were devised in Big Data and Data Mining to solve such an intricate problem. In this paper, we conducted research about the comparison of several similarity measures and community detection algorithms in collaborative filtering for movie recommendation systems. Movielense data set was used to do an empirical experiment. We applied three different similarity measures: Cosine, Euclidean, and Pearson. Moreover, betweenness and eigenvector centrality were used to detect communities from the network. As a result, we elucidated which algorithm is more suitable than its counterpart in terms of recommendation accuracy.
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Acknowledgement
This research was supported by the National Research Foundation of Korea (No. NRF-2022R1A2C1005921) and BK21 FOUR (Fostering Outstanding Universities for Research) (No.5199990914048) and the MSIT(Ministry of Science, ICT), Korea, under the National Program for Excellence in SW, supervised by the IITP(Institute of Information & communications Technology Planning & Evaluation) in 2021"(2021-0-01399)