International Journal of Education and Management Engineering(IJEME)

ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)

Published By: MECS Press

IJEME Vol.10, No.1, Feb. 2020

An Enhanced Data Sparsity Reduction Method for Effective Collaborative Filtering Recommendations

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Abubakar Roko, Abba Almu, Aminu Mohammed, Ibrahim Saidu

Index Terms

collaborative filtering, data sparsity, bi-separated clustering, bi-mean imputation, cold item, rating matrix


Collaborative filtering recommender system suffers from data sparsity problem due to its reliance on numerical ratings to provide recommendations to users. This problem makes it difficult for the system to compute accurate similar neighbours for the items and provide good quality recommendations. Existing methods fail to pre-process the missing ratings of the new items and to predict cold items to the active users which lead to poor quality recommendations. In this work, a sparsity reduction method is presented to improve the quality of recommendations. The method utilises Bi-Separated clustering algorithm to cluster the ratings matrix simultaneously into users and items bi-clusters based on ratings classification. It also employs Bi-Mean Imputation algorithm to fill the missing ratings in the bi-clusters using the estimated means. The method then performs the traditional collaborative filtering process on the new rating matrix for cold items prediction. The experimental results demonstrated that compared to the existing method, the proposed BiSCBiMI improves density of the rating matrix by 5.75%, 10.73% and 7.35% as well as Mean Absolute Error (MAE)  of the new items prediction for all of the considered datasets.  The results indicated that, the proposed approaches are effective in reducing the data sparsity problem as well as items prediction, which in turn returns good quality recommendations. 

Cite This Paper

Abubakar Roko, Abba Almu, Aminu Mohammed, Ibrahim Saidu, " An Enhanced Data Sparsity Reduction Method for Effective Collaborative Filtering Recommendations ", International Journal of Education and Management Engineering(IJEME), Vol.10, No.1, pp.27-42, 2020.DOI: 10.5815/ijeme.2020.01.04


[1] Ardimansyah, I. M., Huda, F. A., and Baizal, A. Z. K.,. Preprocessing Matrix Factorization for Solving Data Sparsity on Memory-based Collaborative Filtering. In Proceedings of the 3rd International Conference on Science in Information Technology (ICSITech), 2017,  pp. 521-525, Bandung, Indonesia.

[2] Burke, R.  Hybrid Recommender Systems. User Modeling and User-Adapted Interaction, 2002, 12(4), 331-370.

[3] Cacheda, F., Carneiro, V., Fernandez, D., and Formoso, V. Comparison of Collaborative Filtering Algorithms: Limitations of Current Techniques and Proposal for Scalable, High-performance Recommender Systems. ACM Transactions on the Web, 2011, 5(1), 1-33.

[4] Cheng J., Zhang L. Jaccard Coefficient-Based Bi-clustering and Fusion Recommender System for Solving Data Sparsity. Advances in Knowledge Discovery and Data Mining, 2009, 1144: 369-380, Springer, Cham.

[5] Chen, Y., Wu, C., Xie, M., and Guo, X. Solving the Sparsity Problem in Recommender Systems Using Association Retrieval. Journal of Computers, 2011, 6(9):1896-1902. 

[6] Chen, Z., Jiang, Y., and Zhao, Y. A Collaborative Filtering Recommendation Algorithm Based on User Interest Change and Trust Evaluation. International Journal of Digital Content Technology and its Applications, 2010, 4(9), 106-113.

[7] Chujai, P., Rasmequan, S., Suksawatchon, U., and Suksawatchon, J. Imputing Missing Values in Collaborative Filtering Using Pattern Frequent Itemsets. In Proceedings of the International Electrical Engineering Congress (iEECON), 2014, pp. 694-697, Chonburi, Thailand.

[8] Goldberg, D., Nichols, D., Oki, B., M., and Douglas, T. Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM, 1992, 35(12), 61-70.

[9] Guo, G., Zhang, J., and Yorke-Smith, N. A Novel Bayesian Similarity Measure for Recommender Systems. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013, pp. 2619-2625, Beijing, China. 

[10] Harper, F., M., and Konstan, J., A. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS), 2015, 5(4), 1-19. 

[11]Khusro, S., Ali, Z., and Ullah, I. Recommender Systems: Issues, Challenges and Research Opportunities. Lecture Notes in Electrical Engineering, 2016, 376, 1179-1189. Springer, Science+Business Media Singapore.

[12] Li, J., Zhang, K., Yang, X., Wei, P., Wang, J., Mitra, K., and Ranjan, R., Category Preferred Canopy–K-Means based Collaborative Filtering Algorithm. Future Generation Computer Systems, 2018, 93: 1046-1054. 

[13] Liang, Z., Bo, X., and Jun, G. A Hybrid Approach to Collaborative Filtering for Overcoming Data Sparsity. In Proceedings of the 9th IEEE International Conference on Signal Processing, 2008, pp.  1596-1599, Beijing, China. 

[14] Linden, G., Smith, B., and York, J. Recommendations: Item-to-Item Collaborative Filtering. IEEE Computer Society, 2003, pp. 76-80.

[15] Ma, H., King, I., and Lyu, R. M. Effective Missing Data Prediction for Collaborative Filtering. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2007, pp. 39-46, Amsterdam, Netherlands.

[16] Miller, B., N., Albert, I., Lam, S., K., Konstan, J., A., and Riedl, J. MovieLens Unplugged: Experiences with an Occasionally Connected Recommender System. Proceedings of the 8th International Conference on Intelligent User Interfaces (IUI), 2003, pp. 263-266, Miami, Florida, USA. 

[17] Najafabadi, K. M., Mahrin, N. M., Chuprat, S., and Sarkan, H. M. Improving the Accuracy of Collaborative Filtering Recommendations Using Clustering and Association Rules Mining on Implicit Data. Computers in Human Behavior, 2017, 67: 113-128.

[18] Pazzani, M., J., and Billsus, D. Content-Based Recommendation Systems. The Adaptive Web-Springer, 2007, 4321: 325-341.

[19] Ping, H. Q., and Ming, X. Research on Several Recommendation Algorithms. Procedia Engineering, 2012, 29: 2427-2431.

[20] Qin, J., Cao, L., and Peng, H. A Solution of Missing Value in Collaborative Filtering Recommendation Algorithm. In Proceedings of the IEEE Chinese Automation Congress (CAC), 2015, pp. 2184-2187, Wuhan, China. 

[21] Resnick, P., and Varian, H. Recommender Systems. Communications of the ACM,       1997, 40( 3):56-58.

[22] Song, M. A Collaborative Filtering Recommendation Algorithm Based on Multi-dimensional Data Filling. In Proceedings of the 2nd IEEE International Conference on Computer and Communications (ICCC), 2016, pp. 175-179, Chengdu, China.

[23] Tan, H., and Ye, H. A Collaborative Filtering Recommendation Algorithm Based on Item Classification. In Proceedings of the IEEE Pacific-Asia Conference on Circuits, Communications and Systems, 2009, pp. 694-697, Chengdu, China. 

[24] Wang, J., Song, H., and Zhou, X. A Collaborative Filtering Recommendation Algorithm Based on Biclustering. In Proceedings of the International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015, pp. 803-807, Shenyang, China.

[25] Xia, W., He, L., Gu, J., and He, K. Effective Collaborative Filtering Approaches Based on Missing Data Imputation. In Proceedings of the Fifth IEEE International Joint Conference on INC, IMS and IDC, 2009, pp. 534-537, Seoul, South Korea. 

[26] Yongsheng, H., Xiangwu, M., and Yujie, Z. A Collaborative Filtering Recommendation Method to the Loyal-user Problem.  In Proceedings of 2nd IEEE International Conference on Computer Science and Information Technology, 2009, pp. 57-60, Beijing, China.