A Customized Non-Exclusive Clustering Algorithm for News Recommendation Systems
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Abstract
Clustering is one of the main tasks in machine learning and data mining and is being utilized in many applications including news recommendation systems. In this paper, we propose a new non-exclusive clustering algorithm named Ordered Clustering (OC) with the aim is to increase the accuracy of news recommendation for online users. The basis of OC is a new initialization technique that groups news items into clusters based on the highest similarities between news items to accommodate news nature in which a news item can belong to different categories. Hence, in OC, multiple memberships in clusters are allowed. An experiment is carried out using a real dataset which is collected from the news websites. The experimental results demonstrated that the OC outperforms the k-means algorithm with respect to Precision, Recall, and F1-Score.
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P. Berkhin, “A survey of clustering data mining techniques,” Proceeding of the Grouping multidimensional data pp. 25-71, 2006.
U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From data mining to knowledge discovery in databases,” AI magazine, vol. 17, no. 3, pp. 37-54, 1996.
T. Hastie, R. Tibshirani, and J. Friedman, “Unsupervised learning,” in The ele-ments of statistical learning, Springer, 2009, pp. 485–585.
W. M. Rand, “Objective criteria for the evaluation of clustering methods,” J. Am. Stat. Assoc., vol. 66, no. 336, pp. 846–850, 1971.
L. Li, D. Wang, T. Li, D. Knox, and B. Padmanabhan, “SCENE: a scalable two-stage personalized news recommendation system,” In Proceedings of the 34th In-ternational ACM SIGIR Conference on Research and Development in Infor-mation Retrieval, 2011, pp. 125-134. ACM.
J. Liu, P. Dolan, and E. R. Pedersen, “Personalized news recommendation based on click behaviour,” In Proceedings of the 15th International Conference on In-telligent User Interfaces, 2010, pp. 31-40. ACM.
L. Zheng, L. Li, W. Hong and T. Li, “PENETRATE: Personalized news recom-mendation using ensemble hierarchical clustering,” Journal of Expert Systems with Applications, vol. 40, no. 6, pp. 2127-2136, 2013.
S. Jiang, and W. Hong, “A vertical news recommendation system: CCNS—An example from Chinese campus news reading system,” In International Conference on Computer Science & Education (ICCSE), 2014, pp. 1105-1114. IEEE.
L. Li, W. Chu, J. Langford, and R. E. Schapire, “A contextual-bandit approach to personalized news article recommendation,” In Proceedings of the 19th Interna-tional Conference on World Wide Web, 2010, pp. 661-670. ACM.
Z Lassoued, and K Abderrahim. "PWARX Model Identification Based on Clus-tering Approach." In Complex System Modelling and Control Through Intelligent Soft Computations, pp. 165-193, 2015. Springer, Cham.
A. Andoni, and P. Indyk, “Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions,” In Proceeding of the 47th Anual IEEE Symposium on Foundations of Computer Science, 2006, pp. 459-468. IEEE.
M. Muja, and D. G. Lowe, “Scalable nearest neighbor algorithms for high dimen-sional data,” Journal of IEEE Transactions on Pattern Analysis & Machine Intelli-gence, vol. 11, pp. 2227-2240, 2014.
A. Z. Broder, “On the resemblance and containment of documents,” In Proceed-ing of the compression and complexity of sequences, 1997, pp. 21-29. IEEE.
E. Cohen, “Size-estimation framework with applications to transitive closure and reachability,” Journal of Computer and System Sciences, vol. 55, no.3, pp. 441-453, 1997.
A. Gionis, P. Indyk, and R. Motwani, “Similarity search in high dimensions via hashing,” In Proceeding of the Very Large Data Base, vol. 99, no. 6, pp. 518-529, 1999.
M. S. Charikar, “Similarity estimation techniques from rounding algoithms,” In Proceedings of the Thiry-fourth annual ACM Symposium on Theory of Com-puting, 2002, pp. 380-388. ACM.
T. Hofmann, “Latent semantic models for collaborative filtering,” Journal of ACM Transactions on Information Systems (TOIS), vol. 22, no. 1, pp. 89-115, 2004.
J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.
A. S. Das, M. Datar, A. Garg, and S. Rajaram, “Google news personalization: scalable online collaborative filtering,” In Proceedings of the 16th International Conference on World Wide Web, 2007, pp. 271-280. ACM.
F. Abel, Q. Gao, G. J. Houben, and K. Tao, “Analyzing user modeling on twitter for personalized news recommendations,” In Proceeding of the International Conference on User Modeling, Adaptation, and Personalization, 2011, pp. 1-12. Springer.