{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T10:56:14Z","timestamp":1784631374185,"version":"3.55.0"},"reference-count":52,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T00:00:00Z","timestamp":1695168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Matrix factorization is a long-established method employed for analyzing and extracting valuable insight recommendations from complex networks containing user ratings. The execution time and computational resources demanded by these algorithms pose limitations when confronted with large datasets. Community detection algorithms play a crucial role in identifying groups and communities within intricate networks. To overcome the challenge of extensive computing resources with matrix factorization techniques, we present a novel framework that utilizes the inherent community information of the rating network. Our proposed approach, named Community-Based Matrix Factorization (CBMF), has the following steps: (1) Model the rating network as a complex bipartite network. (2) Divide the network into communities. (3) Extract the rating matrices pertaining only to those communities and apply MF on these matrices in parallel. (4) Merge the predicted rating matrices belonging to communities and evaluate the root mean square error (RMSE). In our experimentation, we use basic MF, SVD++, and FANMF for matrix factorization, and the Louvain algorithm is used for community division. The experimental evaluation on six datasets shows that the proposed CBMF enhances the quality of recommendations in each case. In the MovieLens 100K dataset, RMSE has been reduced to 0.21 from 1.26 using SVD++ by dividing the network into 25 communities. A similar reduction in RMSE is observed for the datasets of FilmTrust, Jester, Wikilens, Good Books, and Cell Phone.<\/jats:p>","DOI":"10.3390\/e25091360","type":"journal-article","created":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T21:26:32Z","timestamp":1695245192000},"page":"1360","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of Recommendations"],"prefix":"10.3390","volume":"25","author":[{"given":"Srilatha","family":"Tokala","sequence":"first","affiliation":[{"name":"Algorithms and Complexity Theory Lab, Department of Computer Science and Engineering, SRM University-AP, Amaravati 522502, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9029-2187","authenticated-orcid":false,"given":"Murali Krishna","family":"Enduri","sequence":"additional","affiliation":[{"name":"Algorithms and Complexity Theory Lab, Department of Computer Science and Engineering, SRM University-AP, Amaravati 522502, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T. Jaya","family":"Lakshmi","sequence":"additional","affiliation":[{"name":"Algorithms and Complexity Theory Lab, Department of Computer Science and Engineering, SRM University-AP, Amaravati 522502, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hemlata","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Computing, Sheffield Hallam University, Howard Street, Sheffield S1 1WB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2005.99","article-title":"Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions","volume":"17","author":"Adomavicius","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_2","unstructured":"Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reiterer, S., and Stettinger, M. (2014). Recommendation Systems in Software Engineering, Springer."},{"key":"ref_3","unstructured":"Hintz, J. (2023, July 31). Matrix Factorization for Collaborative Filtering Recommender Systems. Available online: https:\/\/www.cs.utexas.edu\/~ans\/pubs\/hintz_f15.pdf."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kumar Bokde, D., Girase, S., and Mukhopadhyay, D. (2015). Role of matrix factorization model in collaborative filtering algorithm: A survey. arXiv.","DOI":"10.1016\/j.procs.2015.04.237"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2009.263","article-title":"Matrix factorization techniques for recommender systems","volume":"42","author":"Koren","year":"2009","journal-title":"Computer"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mehta, R., and Rana, K. (2017, January 7\u20138). A review on matrix factorization techniques in recommender systems. Proceedings of the 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA), Mumbai, India.","DOI":"10.1109\/CSCITA.2017.8066567"},{"key":"ref_7","unstructured":"Abdrabbah, S.B., Ayachi, R., and Amor, N.B. (2014, January 15). Collaborative filtering based on dynamic community detection. Proceedings of the 2nd Workshop on Dynamic Networks and Knowledge Discovery, Nancy, France."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kumar, P., Chawla, P., and Rana, A. (2018, January 6\u20138). A review on community detection algorithms in social networks. Proceedings of the 2018 4th International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Mangalore, India.","DOI":"10.1109\/iCATccT44854.2018.9001978"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1002\/widm.1178","article-title":"Community detection in social networks","volume":"6","author":"Bedi","year":"2016","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Du, N., Wu, B., Pei, X., Wang, B., and Xu, L. (2007, January 12). Community detection in large-scale social networks. Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, San Jose, CA, USA.","DOI":"10.1145\/1348549.1348552"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Karata\u015f, A., and \u015eahin, S. (2018, January 3\u20134). Application areas of community detection: A review. Proceedings of the 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), Ankara, Turkiye.","DOI":"10.1109\/IBIGDELFT.2018.8625349"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lalwani, D., Somayajulu, D.V., and Krishna, P.R. (November, January 29). A community driven social recommendation system. Proceedings of the 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA.","DOI":"10.1109\/BigData.2015.7363828"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Guo, W., Gao, H., Shi, J., Long, B., Zhang, L., Chen, B.C., and Agarwal, D. (2019, January 4\u20138). Deep natural language processing for search and recommender systems. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3332290"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1007\/s11257-020-09270-8","article-title":"Generating post hoc review-based natural language justifications for recommender systems","volume":"31","author":"Musto","year":"2021","journal-title":"User Model. -User-Adapt. Interact."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, S., Owusu, S., and Zhou, L. (2013, January 8\u201314). Social network based recommendation systems: A short survey. Proceedings of the 2013 International Conference on Social Computing, Alexandria, VA, USA.","DOI":"10.1109\/SocialCom.2013.134"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.jss.2014.09.019","article-title":"Recommender systems based on social networks","volume":"99","author":"Sun","year":"2015","journal-title":"J. Syst. Softw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/S1567-4223(02)00022-4","article-title":"A personalized recommendation procedure for Internet shopping support","volume":"1","author":"Kim","year":"2002","journal-title":"Electron. Commer. Res. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wei, K., Huang, J., and Fu, S. (2007, January 9\u201311). A survey of e-commerce recommender systems. Proceedings of the 2007 International Conference on Service Systems and Service Management, Chengdu, China.","DOI":"10.1109\/ICSSSM.2007.4280214"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1016\/j.eswa.2006.12.025","article-title":"A recommender system using GA K-means clustering in an online shopping market","volume":"34","author":"Kim","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.chb.2017.11.020","article-title":"Excessive use of online video streaming services: Impact of recommender system use, psychological factors, and motives","volume":"80","author":"Hasan","year":"2018","journal-title":"Comput. Hum. Behav."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10059","DOI":"10.1016\/j.eswa.2012.02.038","article-title":"A literature review and classification of recommender systems research","volume":"39","author":"Park","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1097\/MPG.0000000000001454","article-title":"Complementary feeding: A position paper by the European Society for Paediatric Gastroenterology, Hepatology, and Nutrition (ESPGHAN) Committee on Nutrition","volume":"64","author":"Fewtrell","year":"2017","journal-title":"J. Pediatr. Gastroenterol. Nutr."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Isinkaye, F.O. (2021). Matrix factorization in recommender systems: Algorithms, applications, and peculiar challenges. IETE J. Res., 1\u201314.","DOI":"10.1080\/03772063.2021.1997357"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1023\/A:1009804230409","article-title":"E-commerce recommendation applications","volume":"5","author":"Schafer","year":"2001","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1177\/1461444814538646","article-title":"Recommended for you: The Netflix Prize and the production of algorithmic culture","volume":"18","author":"Hallinan","year":"2016","journal-title":"New Media Soc."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1002\/env.3170050203","article-title":"Positive matrix factorization: A nonnegative factor model with optimal utilization of error estimates of data values","volume":"5","author":"Paatero","year":"1994","journal-title":"Environmetrics"},{"key":"ref_27","unstructured":"Mnih, A., and Salakhutdinov, R.R. (2023, July 31). Probabilistic matrix factorization. Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2007\/file\/d7322ed717dedf1eb4e6e52a37ea7bcd-Paper.pdf."},{"key":"ref_28","first-page":"17","article-title":"The singular value decomposition (svd) in tensors (multidimensional arrays) as an optimization problem. solution via genetic algorithms and method of nelder-mead","volume":"6","author":"Mastorakis","year":"2007","journal-title":"WSEAS Trans. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hu, Y., Koren, Y., and Volinsky, C. (2008, January 15\u201319). Collaborative filtering for implicit feedback datasets. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy.","DOI":"10.1109\/ICDM.2008.22"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shi, X., Lu, H., He, Y., and He, S. (2015, January 25\u201328). Community detection in social network with pairwisely constrained symmetric nonnegative matrix factorization. Proceedings of the Proceedings of the 2015 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, Paris, France.","DOI":"10.1145\/2808797.2809383"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xue, H.J., Dai, X., Zhang, J., Huang, S., and Chen, J. (2017, January 19\u201325). Deep matrix factorization models for recommender systems. Proceedings of the IJCAI, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/447"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Schlichtkrull, M., Kipf, T.N., Bloem, P., Van Den Berg, R., Titov, I., and Welling, M. (2018, January 3\u20137). Modeling relational data with graph convolutional networks. Proceedings of the The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece. Proceedings 15.","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1016\/j.patrec.2019.07.005","article-title":"Regularized asymmetric nonnegative matrix factorization for clustering in directed networks","volume":"125","author":"Tosyali","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7821","DOI":"10.1073\/pnas.122653799","article-title":"Community structure in social and biological networks","volume":"99","author":"Girvan","year":"2002","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"036106","DOI":"10.1103\/PhysRevE.76.036106","article-title":"Near linear time algorithm to detect community structures in large-scale networks","volume":"76","author":"Raghavan","year":"2007","journal-title":"Phys. Rev. E"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","article-title":"Fast unfolding of communities in large networks","volume":"2008","author":"Blondel","year":"2008","journal-title":"J. Stat. Mech. Theory Exp."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1016\/j.tcs.2010.11.041","article-title":"Post-processing hierarchical community structures: Quality improvements and multi-scale view","volume":"412","author":"Pons","year":"2011","journal-title":"Theor. Comput. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5233","DOI":"10.1038\/s41598-019-41695-z","article-title":"From Louvain to Leiden: Guaranteeing well-connected communities","volume":"9","author":"Traag","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kumar, R., Verma, B., and Rastogi, S.S. (2014). Social popularity based SVD++ recommender system. Int. J. Comput. Appl., 87.","DOI":"10.5120\/15279-4033"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rendle, S. (2010, January 13\u201317). Factorization machines. Proceedings of the 2010 IEEE International Conference on Data Mining, Sydney, Australia.","DOI":"10.1109\/ICDM.2010.127"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"102694","DOI":"10.1016\/j.ipm.2021.102694","article-title":"A novel regularized asymmetric nonnegative matrix factorization for text clustering","volume":"58","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","article-title":"Learning the parts of objects by non-negative matrix factorization","volume":"401","author":"Lee","year":"1999","journal-title":"Nature"},{"key":"ref_43","unstructured":"Alzahrani, T., and Horadam, K.J. (2015). Complex Systems and Networks: Dynamics, Controls and Applications, Springer."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2050408","DOI":"10.1142\/S0217984920504084","article-title":"Recent trends on community detection algorithms: A survey","volume":"34","author":"Gupta","year":"2020","journal-title":"Mod. Phys. Lett. B"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"He, X., Zhang, H., Kan, M.Y., and Chua, T.S. (2016, January 17\u201321). Fast matrix factorization for online recommendation with implicit feedback. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, Pisa, Italy.","DOI":"10.1145\/2911451.2911489"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1109\/TCSS.2020.2964197","article-title":"C-blondel: An efficient Louvain-based dynamic community detection algorithm","volume":"7","author":"Seifikar","year":"2020","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_47","unstructured":"(2023, July 31). Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/prajitdatta\/movielens-100k-dataset."},{"key":"ref_48","unstructured":"(2023, July 31). Konect. Available online: https:\/\/www.kaggle.com\/datasets\/tranhungnghiep\/goodbooks6m."},{"key":"ref_49","unstructured":"(2023, July 31). Konect. Available online: https:\/\/www.kaggle.com\/datasets\/meirnizri\/cellphones-recommendations."},{"key":"ref_50","unstructured":"(2023, July 31). Konect. Available online: http:\/\/konect.cc\/networks\/librec-filmtrust-ratings\/."},{"key":"ref_51","unstructured":"(2023, July 31). Konect. Available online: http:\/\/konect.cc\/networks\/Jester2\/."},{"key":"ref_52","unstructured":"(2023, July 31). Konect. Available online: http:\/\/konect.cc\/networks\/Wikilens-ratings\/."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/9\/1360\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:54:02Z","timestamp":1760129642000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/9\/1360"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,20]]},"references-count":52,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["e25091360"],"URL":"https:\/\/doi.org\/10.3390\/e25091360","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,20]]}}}