Main Article Content

Noor Ifada Susi Susanti Mulaab


The Collaborative Filtering (CF) widely used in Recommendation System commonly suffers the sparsity issue since the unobserved rating entries usually over dominance the observed ones. A clustering technique is an alternative solution that can solve the problem. However, no in-depth work has investigated how the missing entries should be mitigated and how the cluster-based approach can be implemented. In this study, we show how the imputed cluster-based approach deals with the missing entries, improving the recommendation quality. The framework of our method consists of four main stages: rating imputation to replace the missing entries, K-means clustering to group users or items based on the imputed rating data, CF-based prediction model, and generating the list of top-N recommendation. This paper uses three variations of imputation techniques, i.e., null, mean, and mode. The cluster-based approach is employed
by using the K-Means as the clustering technique, and either the user-based or the items-based model as the CF approach. Experiment results show that the null imputation technique gives the best results when dealing with the missing entries. This finding indicates that the implementation of the clustering technique
is sufficient for solving the sparsity issue such that imputing the missing entries is not necessary. We also show that our imputed cluster-based CF methods always outperform the traditional CF methods in terms of the F1-Score metric.

Article Details

How to Cite
IFADA, Noor; SUSANTI, Susi; , Mulaab. IMPACT OF IMPUTATION ON CLUSTER-BASED COLLABORATIVE FILTERING APPROACH FOR RECOMMENDATION SYSTEM. Jurnal Ilmiah Kursor, [S.l.], v. 10, n. 1, nov. 2019. ISSN 2301-6914. Available at: <>. Date accessed: 22 sep. 2020. doi:


[1] J. A. Konstan and J. Riedl, "Recommender systems: from algorithms to user experience," User Modeling and User-Adapted Interaction, vol. 22, pp. 101-123, 2012.
[2] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based Collaborative Filtering Recommendation Algorithms," in Proceeding of The 10th International Conference on World Wide Web, Hong Kong, 2001, pp. 285-295.
[3] S. J. Gong, "Employing user attribute and item attribute to enhance the collaborative filtering recommendation," Journal of Software, vol. 4, pp. 883-890, 2009.
[4] C. C. Aggarwal, Recommender Systems: The Textbook. Switzerland: Springer International Publishing, 2016.
[5] M. Papagelis and D. Plexousakis, "Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents," Engineering Applications of Artificial Intelligence vol. 18, pp. 781-789, 2005.
[6] B. M. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Recommender Systems for Large-scale E-Commerce: Scalable Neighborhood Formation Using Clustering," in Proceeding of The 5th International Conference on Computer and Information Technology, Dhaka, Bangladesh, 2002, pp. 23-28.
[7] H. Koohi and K. Kiani, "User based Collaborative Filtering using fuzzy C-means," Measurement, vol. 91, pp. 134-139, 2016.
[8] N. P. Kumar and Z. Fan, "Hybrid User-Item Based Collaborative Filtering," Procedia Computer Science, vol. 60, pp. 1453-1461, 2015.
[9] P. Phorasim and L. Yu, "Movies recommendation system using collaborative filtering and k-means," International Journal of Advanced Computer Research, vol. 7, pp. 52-59, 2017.
[10] G. M. Dakhel and M. Mahdavi, "A new collaborative filtering algorithm using K-means clustering and neighbors' voting," in Proceeding of The 11th International Conference on Hybrid Intelligent Systems, 2011, pp. 179-184.
[11] S. Wei, N. Ye, S. Zhang, X. Huang, and J. Zhu, "Collaborative filtering recommendation algorithm based on item clustering and global similarity," in Proceeding of The 5th International Conference on Business Intelligence and Financial Engineering (BIFE), Lanzhou, China, 2012, pp. 69-72.
[12] J. Han, M. Kamber, and J. Pei, Data mining: concepts and techniques, Third ed. Waltham, USA: Morgan Kaufmann, 2012.
[13] G. Adomavicius and A. Tuzhilin, "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions," IEEE Transactions on Knowledge and Data Engineering, vol. 17, pp. 734-749, 2005.