INFORMATION CHANGE THE WORLD

International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.14, No.4, Aug. 2022

An Improved Popular Items Extraction for Covering Reduction Collaborative Filtering

Full Text (PDF, 663KB), PP.32-45


Views:1   Downloads:0

Author(s)

Abubakar Roko, Umar Muhammad Bello, Abba Almu

Index Terms

Covering Reduction, Popular Items, Sparsity, Popular Items Extraction Algorithm, Collaborative Filtering.

Abstract

Recommender Systems are systems that aid users in finding relevant items, products, or services, usually in an online setting. Collaborative Filtering is the most popular approach for building recommender system due to its superior performance. There are several collaborative filtering methods developed, however, all of them have an inherent problem of data sparsity. Covering Reduction Collaborative Filtering (CRCF) is a new collaborative filtering method developed to solve the problem. CRCF has a key feature called popular items extraction algorithm which produces a list of items with the most ratings, however, the algorithm fails in a denser dataset because it allows any item to be in the list. Likewise, the algorithm does not consider the rating values of items while considering the popular items. These make it to produce less accurate recommendation. This research extends CRCF by developing a new popular item extraction algorithm that removes items with low modal ratings and similarly utilizes the rating values in considering the popular items. This newly developed method is incorporated in CRCF and the new method is called Improved Popular Items Extraction for Covering Reduction Collaborative Filtering (ICRCF).  Experiment was conducted on Movielens-1M and Movielens-10M datasets using precision, recall and f1-score as performance metrics. The result of the experiment shows that the new method, ICRCF provides a better recommendation than the base method CRCF in all the performance metrics. Furthermore, the new method is able to perform well both at higher and lower levels of sparsity.

Cite This Paper

Abubakar Roko, Umar Muhammad Bello, Abba Almu, "An Improved Popular Items Extraction for Covering Reduction Collaborative Filtering", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.14, No.4, pp. 32-45, 2022. DOI:10.5815/ijigsp.2022.04.03

Reference

[1]J. Li et al., “Category Preferred Canopy–K-means based Collaborative Filtering algorithm,” Futur. Gener. Comput. Syst., vol. 93, pp. 1046–1054, 2019, doi: 10.1016/j.future.2018.04.025.

[2]C. Porcel, J. M. Morales Del Castillo, M. J. Cobo, A. A. Ruiz, and E. Herrera-Viedma, “An improved recommender system to avoid the persistent information overload in a university digital library,” Control Cybern., vol. 39, no. 4, pp. 898–923, 2010.

[3]H. Costa and L. Macedo, “Emotion-based recommender system for overcoming the problem of information overload,” Commun. Comput. Inf. Sci., vol. 365, pp. 178–189, 2013, doi: 10.1007/978-3-642-38061-7_18.

[4]A. Roko, A. Almu, A. Mohammed, and I. Saidu, “An Enhanced Data Sparsity Reduction Method for Effective Collaborative Filtering Recommendations,” Int. J. Educ. Manag. Eng., vol. 10, no. 1, pp. 27–42, 2020.

[5]F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Recommender System Handbook. 2011.

[6]J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, “Recommender system application developments: A survey,” Decis. Support Syst., vol. 74, pp. 12–32, 2015, doi: 10.1016/j.dss.2015.03.008.

[7]J. Wei, J. He, K. Chen, Y. Zhou, and Z. Tang, “Collaborative filtering and deep learning based recommendation system for cold start items,” Expert Syst. Appl., vol. 69, pp. 1339–1351, 2017, doi: 10.1016/j.eswa.2016.09.040.

[8]S. Natarajan, S. Vairavasundaram, S. Natarajan, and A. H. Gandomi, “Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data,” Expert Syst. Appl., vol. 149, 2020, doi: 10.1016/j.eswa.2020.113248.

[9]A. M. Jones, A. Arya, P. Agarwal, P. Gaurav, and T. Arya, “An Ontological Sub-Matrix Factorization based Approach for Cold-Start Issue in Recommender Systems,” Int. Conf. Curr. Trends Comput. Electr. Electron. Commun. CTCEEC 2017, no. December 2019, pp. 161–166, 2018, doi: 10.1109/CTCEEC.2017.8455147.

[10]A. Angadi, S. K. Gorripati, and P. Suresh Varma, “Temporal community-based collaborative filtering to relieve from cold-start and sparsity problems,” Int. J. Intell. Syst. Appl., vol. 10, no. 10, pp. 53–62, 2018, doi: 10.5815/ijisa.2018.10.06.

[11]B. S. Neysiani, N. Soltani, R. Mofidi, and M. H. Nadimi-Shahraki, “Improve Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm,” Int. J. Inf. Technol. Comput. Sci., vol. 11, no. 2, pp. 48–55, 2019, doi: 10.5815/ijitcs.2019.02.06.

[12]M. K. Najafabadi, M. N. ri Mahrin, S. Chuprat, and H. M. Sarkan, “Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data,” Comput. Human Behav., vol. 67, pp. 113–128, 2017, doi: 10.1016/j.chb.2016.11.010.

[13]L. Candillier, F. Meyer, and M. Boullé, “Comparing state-of-the-art collaborative filtering systems,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 4571 LNAI, pp. 548–562, 2007, doi: 10.1007/978-3-540-73499-4_41.

[14]M. D. Ekstrand, J. T. Riedl, and J. A. Konstan, “Collaborative filtering recommender systems,” Found. Trends Human-Computer Interact., vol. 4, no. 2, pp. 81–173, 2010, doi: 10.1561/1100000009.

[15]J. Huttner, “From Tapestry to SVD: A Survey of the Algorithms That Power Recommender Systems,” no. May 2009, p. 32, 2009, [Online]. Available: http://thesis.haverford.edu/dspace/handle/10066/3706.

[16]M. A. Ghazanfar and A. Prügel-Bennett, “Building switching hybrid recommender system using machine learning classifiers and collaborative filtering,” IAENG Int. J. Comput. Sci., vol. 37, no. 3, 2010.

[17]M. K. K. Devi, R. T. Samy, S. V. Kumar, and P. Venkatesh, “Probabilistic neural network approach to alleviate sparsity and cold start problems in collaborative recommender systems,” 2010 IEEE Int. Conf. Comput. Intell. Comput. Res. ICCIC 2010, pp. 286–289, 2010, doi: 10.1109/ICCIC.2010.5705777.

[18]Y. Chen, C. Wu, M. Xie, and X. Guo, “Solving the sparsity problem in recommender systems using association retrieval,” J. Comput., vol. 6, no. 9, pp. 1896–1902, 2011, doi: 10.4304/jcp.6.9.1896-1902.

[19]J. Bobadilla, F. Ortega, A. Hernando, and J. Alcalá, “Improving collaborative filtering recommender system results and performance using genetic algorithms,” Knowledge-Based Syst., vol. 24, no. 8, pp. 1310–1316, 2011, doi: 10.1016/j.knosys.2011.06.005.

[20]G. Guo, J. Zhang, and D. Thalmann, “User Modeling, Adaptation, and Personalization: 20th International Conference, UMAP 2012, Montreal, Canada, July 16-20, 2012. Proceedings,” User Model. Adapt. …, no. Imi, pp. 114–125, 2012, [Online]. Available: http://dx.doi.org/10.1007/978-3-642-31454-4_10%5Cnhttp://link.springer.com/chapter/10.1007/978-3-642-31454-4_10.

[21]H. Sobhanam and A. K. Mariappan, “Addressing cold start problem in recommender systems using association rules and clustering technique,” 2013 Int. Conf. Comput. Commun. Informatics, ICCCI 2013, pp. 0–4, 2013, doi: 10.1109/ICCCI.2013.6466121.

[22]S. Augustin, K. Niemann, and M. Wolpers, “A New Collaborative Filtering Approach for Increasing the Aggregate Diversity of Recommender Systems,” pp. 955–963, 2013.

[23]D. Zhang, C. H. Hsu, M. Chen, Q. Chen, N. Xiong, and J. Lloret, “Cold-start recommendation using Bi-clustering and fusion for large-scale social recommender systems,” IEEE Trans. Emerg. Top. Comput., vol. 2, no. 2, pp. 239–250, 2014, doi: 10.1109/TETC.2013.2283233.

[24]F. Xie, Z. Chen, J. Shang, W. Huang, and J. Li, “Item similarity learning methods for collaborative filtering recommender systems,” Proc. - Int. Conf. Adv. Inf. Netw. Appl. AINA, vol. 2015-April, pp. 896–903, 2015, doi: 10.1109/AINA.2015.285.

[25]J. Cheng and L. Zhang, Jaccard coefficient-based bi-clustering and fusion recommender system for solving data sparsity, vol. 11440 LNAI. Springer International Publishing, 2019.

[26]Z. Zhang, Y. Zhang, and Y. Ren, “Employing neighborhood reduction for alleviating sparsity and cold start problems in user-based collaborative filtering,” Inf. Retr. J., vol. 23, no. 4, pp. 449–472, 2020, doi: 10.1007/s10791-020-09378-w.

[27]B. J. Schafer, D. Frankowski, J. Herlocker, and S. Sen, “Collaborative Filtering Recommender Systems - CollaborativeFilteringRecommenderSystems.pdf,” Lncs, vol. 4321, no. January 2007, pp. 291–324, 2007, [Online]. Available: http://www.eui.upm.es/~jbobi/jbobi/PapersRS/CollaborativeFilteringRecommenderSystems.pdf.

[28]Q. Z. Kong and Z. X. Wei, “Covering-based fuzzy rough sets,” J. Intell. Fuzzy Syst., vol. 29, no. 6, pp. 2405–2411, 2015, doi: 10.3233/IFS-151940.