The User Personalization with KNN for Recommender System

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Arie Satia Dharma

Abstract

Following the increase in of the information available on the Web, it is important to diversity of its users and the complexity of Web applications. One web application that has a diversity of users is a news website. Customizing a website with the characteristics of each user is called personalization. The purpose of this study is to study the methods used in giving news recommendations using user personalization. Collaborative filtering method (CF) is one method that groups users based on the nature of the user. This CF method can be applied using the k-nearest neighbor (KNN) algorithm. The proximity between users in this algorithm is sought using the Pearson correlation technique and cosine correlation. The best technique by considering the smallest value of prediction error evaluation will be applied to giving recommendations. Evaluation of these errors was tested by applying the formula Root Mean Square Error. The best evaluation results obtained in this study are the k-nearest neighbor algorithm with cosine correlation similiarity.

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How to Cite
DHARMA, Arie Satia. The User Personalization with KNN for Recommender System. SinkrOn, [S.l.], v. 3, n. 2, p. 45-48, mar. 2019. ISSN 2541-2019. Available at: <http://jurnal.polgan.ac.id/index.php/sinkron/article/view/10047>. Date accessed: 21 july 2019. doi: https://doi.org/10.33395/sinkron.v3i2.10047.
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References

[1] Gorakala, K. S, Building Recommendation Engines, Birmingham: Packt Publishing, 2016.

[2] Saranya.K.G, & G.Sudha Sadhasivam, P, “A personalized online news recommendation system,” International Journal of Computer Applications, pp. 6-13, 2012.

[3] F. Isinkaye, Y. Folajimi, and B. Ojokoh, “Recommendation systems: principles, methods and evaluation,” Egyptian Informatics Journal, pp. 261-273, 2015.

[4] H. H. Sarirah, “News recommendation based on web usage and web content mining.” IEEE Transl. International Conference on Data Engineering Workshops (ICDEW), pp. 326-329, 2013.

[5] Chaturvedi, A. K, “Recommender system for news articles using supervised learning,” Department of Information and Communications Technologies - Universitat Pompeu Fabra, 2017.

[6] S.Kaur, and E. M. Rashid, “Web news mining using Back Propagation Neural Network and clustering using K-Means algorithm in big data,” Indian Journal of Science and Technology, pp. 1-8, 2016.

[7] H. Hasija and D. Chaurasia. “Recommender system with web usage mining based on Fuzzy C Means and Neural Networks,” IEEE transl. International Conference on Next Generation Computing Technologies (NGCT), pp. 768-772, 2015.

[8] X. Su and T. M. Khoshgoftaar, “A survey of collaborative filtering techniques,” Advances in Artificial Intelligence, pp. 2-2, January 2009.

[9] R. Suguna, and D. Sharmila, “An efficient web recommendation system using collaborative filtering and pattern discovery algorithms,” International Journal of Computer Applications, pp. 37-42, 2013.

[10] Sridhar, B., & Khan, M. Z, “RMSE comparison of Path Loss Models for UHF/VHF bands in India,” IEEE, pp. 330-335, 2014.

[11] S. Yang , M. Korayem , K. AlJadda , T. Grainger , and S. Natarajan, “Combining content-based and collaborative filtering for job recommendation system,” Knowledge-Based Systems, pp. 37-45, November 2017.

[12] Chintan R. Varnagar, N. N, “Web usage mining: A review on process methods and techniques,” IEEE Transl. Proceedings of the International Conference on Information Communication and Embedded Systems (ICICES), pp. 40-46, Feb 2013.

[13] Pandya, R, “Web usage mining with personalization on social web,” International Journal of Engineering Trends and Technology, pp. 325-328, 2015.