Abstract
Many types of research have been conducted in recommendation system to develop approaches to solve the challenges for collaborative filtering problem such as cold start problem; in this paper, we proposed the approach to solving the problem of collaborative by using social network analysis and k-nearest neighbor (k-NN). We used the centrality of social network to detect the community or cluster group to the user, and then apply the k-NN method to find a group for new users with similar personal information such as age, gender, and occupation after that recommendation system will recommend items that users in the group were previously interested for the new.
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Acknowledgements
This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under ITRC (Information Technology Research Center) support program (IITP-2018-2014-1-00720) supervised by IITP (Institute for Information & communications Technology Promotion) and the National Research Foundation of Korea (No. NRF-2017R1A2B1008421).
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Xinchang, K., Park, DS., Vilakone, P. (2020). Movie Recommendation System Using Social Network Analysis and k-Nearest Neighbor. In: Park, J., Park, DS., Jeong, YS., Pan, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2018 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-9341-9_104
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DOI: https://doi.org/10.1007/978-981-13-9341-9_104
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