Personalized Movie Recommendation System Combining Data Mining with the k-Clique Method


Phonexay Vilakone, Khamphaphone Xinchang, Doo-Soon Park, Journal of Information Processing Systems Vol. 15, No. 5, pp. 1141-1155, Oct. 2019  

https://doi.org/10.3745/JIPS.04.0138
Keywords: association rule mining, k-Cliques, Recommendation System
Fulltext:

Abstract

Today, most approaches used in the recommendation system provide correct data prediction similar to the data that users need. The method that researchers are paying attention and apply as a model in the recommendation system is the communities’ detection in the big social network. The outputted result of this approach is effective in improving the exactness. Therefore, in this paper, the personalized movie recommendation system that combines data mining for the k-clique method is proposed as the best exactness data to the users. The proposed approach was compared with the existing approaches like k-clique, collaborative filtering, and collaborative filtering using k-nearest neighbor. The outputted result guarantees that the proposed method gives significant exactness data compared to the existing approach. In the experiment, the MovieLens data were used as practice and test data.


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Cite this article
[APA Style]
Vilakone, P., Xinchang, K., & Park, D. (2019). Personalized Movie Recommendation System Combining Data Mining with the k-Clique Method. Journal of Information Processing Systems, 15(5), 1141-1155. DOI: 10.3745/JIPS.04.0138.

[IEEE Style]
P. Vilakone, K. Xinchang, D. Park, "Personalized Movie Recommendation System Combining Data Mining with the k-Clique Method," Journal of Information Processing Systems, vol. 15, no. 5, pp. 1141-1155, 2019. DOI: 10.3745/JIPS.04.0138.

[ACM Style]
Phonexay Vilakone, Khamphaphone Xinchang, and Doo-Soon Park. 2019. Personalized Movie Recommendation System Combining Data Mining with the k-Clique Method. Journal of Information Processing Systems, 15, 5, (2019), 1141-1155. DOI: 10.3745/JIPS.04.0138.