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http://dx.doi.org/10.6109/jkiice.2017.21.2.277

Clustering-Based Recommendation Using Users' Preference  

Kim, Younghyun (Department of Computer Science and Engineering, Dankook University)
Shin, Won-Yong (Department of Computer Science and Engineering, Dankook University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.21, no.2, 2017 , pp. 277-284 More about this Journal
Abstract
In a flood of information, most users will want to get a proper recommendation. If a recommender system fails to give appropriate contents, then quality of experience (QoE) will be drastically decreased. In this paper, we propose a recommender system based on the intra-cluster users' item preference for improving recommendation accuracy indices such as precision, recall, and F1 score. To this end, first, users are divided into several clusters based on the actual rating data and Pearson correlation coefficient (PCC). Afterwards, we give each item an advantage/disadvantage according to the preference tendency by users within the same cluster. Specifically, an item will be received an advantage/disadvantage when the item which has been averagely rated by other users within the same cluster is above/below a predefined threshold. The proposed algorithm shows a statistically significant performance improvement over the item-based collaborative filtering algorithm with no clustering in terms of recommendation accuracy indices such as precision, recall, and F1 score.
Keywords
Recommender system; Clustering; Pearson correlation coefficient; Precision; Recall; F1 score;
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  • Reference
1 D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich, "Recommender systems: An introduction," Cambridge University Press, 2010.
2 J. A. Konstan and J. Riedl, "Recommender systems: From algorithms to user experience," Springer User Modeling and User-Adapted Interaction, vol. 22, no. 1, pp. 101-123, March 2012.   DOI
3 X. Su and T. M. Khoshgoftaar, "A survey of collaborative filtering techniques," Advances in Artificial Intelligence, vol. 2009, no. 421425, pp. 1-19, 2009.
4 M. J. Pazzani and D. Billsus, "Content-based recommendation systems," Springer Lecture Notes in Computer Science, vol. 4321, pp. 325-341, 2007.
5 Netflix Prize, http://www.netflixprize.com/
6 Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, 521, pp. 436-444, May 2015.   DOI
7 P. M. Napol, "Special issue introduction: Big data and media management," International Journal on Media Management, vol. 18, no. 1, pp. 1-7, June 2016.   DOI
8 M. S. Berrie, "Curatorial compass: Organising meaning in institutional and online displays," Museological Review, vol. 18, no. 1, pp. 61-68, 2014.
9 X. Wang, X. Wang, Z. Ding, X. Nie, and L. Xiao, "A new algorithm based on item clustering and matrix factorization," International Journal of Engineering and Technology, vol. 9, no. 2, pp. 160-165, January 2017.   DOI
10 C. Cheng, X. Wang, Z. Li, and Y. Lin, "A new TV recommendation algorithm based on interest quantification and item clustering," in Proceedings of the IEEE ICSESS, Beijing, China, pp. 215-200, September 2015.
11 W. H. Gomaa and A. A. Fahmy, "A survey of text similarity approaches," International Journal of Computer Applications, vol. 68, no. 13, pp. 13-18, April 2013.   DOI
12 U. Luxburg, "A tutorial on spectral clustering," Springer Statistics and Computing, vol. 17, no. 4, pp. 395-416, December 2007.   DOI
13 J. Lee, D. Lee, Y.-C. Lee, W.-S. Hwang, and S.-W. Kim, "Improving the accuracy of top-N recommendation using a preference model," Information Sciences, vol. 348, no. 20, pp. 290-304, June 2016.   DOI
14 J. Schaffer, T. Hollerer, and J. O'Donovan, "Hypothetical recommendation: A study of interactive profile manipulation behavior for recommender systems," in Proceedings of the FLAIRS, Hollywood, USA, pp. 507- 512, May 2015.
15 D. Song and D. A. Meyer, "Recommending positive links in signed social networks by optimizing a generalized AUC," in Proceedings of the AAAI, Austin, USA, pp. 290-296, January 2015.