{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:31:45Z","timestamp":1760059905085,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T00:00:00Z","timestamp":1752969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>In the realm of recommender systems, the prediction of diverse customer preferences has emerged as a compelling research challenge, particularly for multi-state business organizations operating across various geographical regions. Collaborative filtering, a widely utilized recommendation technique, has demonstrated its efficacy in sectors such as e-commerce, tourism, hotel management, and entertainment-based customer services. In the item-based collaborative filtering approach, users\u2019 evaluations of purchased items are considered uniformly, without assigning weight to the participatory data sources and users\u2019 ratings. This approach results in the \u2018relevance problem\u2019 when assessing the generated recommendations. In such scenarios, filtering collaborative patterns based on regional and local characteristics, while emphasizing the significance of branches and user ratings, could enhance the accuracy of recommendations. This paper introduces a turnover-based weighting model utilizing a big data processing framework to mine multi-level collaborative filtering patterns. The proposed weighting model assigns weights to participatory data sources based on the turnover cost of the branches, where turnover refers to the revenue generated through total business transactions conducted by the branch. Furthermore, the proposed big data framework eliminates the forced integration of branch data into a centralized repository and avoids the complexities associated with data movement. To validate the proposed work, experimental studies were conducted using a benchmarking dataset, namely the \u2018Movie Lens Dataset\u2019. The proposed approach uncovers multi-level collaborative pattern bases, including global, sub-global, and local levels, with improved predicted ratings compared with results generated by traditional recommender systems. The findings of the proposed approach would be highly beneficial to the strategic management of an interstate business organization, enabling them to leverage regional implications from user preferences.<\/jats:p>","DOI":"10.3390\/computers14070294","type":"journal-article","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T08:47:14Z","timestamp":1753087634000},"page":"294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Multi-Level Recommender System Using Turnover-Based Weighting for Predicting Regional Preferences"],"prefix":"10.3390","volume":"14","author":[{"given":"Venkatesan","family":"Thillainayagam","sequence":"first","affiliation":[{"name":"Department of Computer Applications, A.V.C. College of Engineering, Mayiladuthurai 609305, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2798-8007","authenticated-orcid":false,"given":"Ramkumar","family":"Thirunavukarasu","sequence":"additional","affiliation":[{"name":"School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8177-3096","authenticated-orcid":false,"given":"J. Arun","family":"Pandian","sequence":"additional","affiliation":[{"name":"School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, India"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.neucom.2017.01.026","article-title":"Machine learning on big data: Opportunities and challenges","volume":"237","author":"Zhou","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhang, C., Wu, X., Zhang, S., Zhang, C., and Wu, X. (2004). Data mining and multi-database mining. Knowledge Discovery in Multiple Databases, Springer Nature.","DOI":"10.1007\/978-0-85729-388-6"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/widm.1077","article-title":"A survey on mining multiple data sources","volume":"3","author":"Ramkumar","year":"2013","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s12525-016-0219-0","article-title":"Big data analytics in E-commerce: A systematic review and agenda for future research","volume":"26","author":"Akter","year":"2016","journal-title":"Electron. Mark."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1093\/nsr\/nwt032","article-title":"Challenges of big data analysis","volume":"1","author":"Fan","year":"2014","journal-title":"Natl. Sci. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Latha Bhaskaran, K., Osei, R.S., Kotei, E., Agbezuge, E.Y., Ankora, C., and Ganaa, E.D. (2022). A survey on big data in pharmacology, toxicology and pharmaceutics. Big Data Cogn. Comput., 6.","DOI":"10.3390\/bdcc6040161"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s10660-016-9242-7","article-title":"Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research","volume":"17","author":"Tian","year":"2017","journal-title":"Electron. Commer. Res."},{"key":"ref_8","first-page":"97","article-title":"Data mining with big data","volume":"26","author":"Wu","year":"2013","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Uzun-Per, M., Can, A.B., G\u00fcrel, A.V., and Akta\u015f, M.S. (2021, January 15\u201318). Big data testing framework for recommendation systems in e-science and e-commerce domains. Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA.","DOI":"10.1109\/BigData52589.2021.9672082"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1145\/1327452.1327492","article-title":"MapReduce: Simplified data processing on large clusters","volume":"51","author":"Dean","year":"2008","journal-title":"Commun. ACM"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s40537-015-0030-3","article-title":"Big data analytics: A survey","volume":"2","author":"Tsai","year":"2015","journal-title":"J. Big Data"},{"key":"ref_12","first-page":"177","article-title":"High Frequency Rule Synthesis in a Large Scale Multiple Database with MapReduce","volume":"68","author":"Bisoyi","year":"2022","journal-title":"Int. J. Electron. Telecommun."},{"key":"ref_13","unstructured":"Good, I.J. (1950). Probability and the Weighing of Evidence, C. Griffin."},{"key":"ref_14","first-page":"47","article-title":"Post mining-Discovering valid rules from different sized data sources","volume":"3","author":"Nedunchezhian","year":"2007","journal-title":"Int. J. Inf. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.patrec.2007.09.001","article-title":"Synthesizing heavy association rules from different real data sources","volume":"29","author":"Adhikari","year":"2008","journal-title":"Pattern Recognit. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s10115-008-0126-6","article-title":"Modified algorithms for synthesizing high-frequency rules from different data sources","volume":"17","author":"Ramkumar","year":"2008","journal-title":"Knowl. Inf. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1109\/TKDE.2003.1185839","article-title":"Synthesizing high-frequency rules from different data sources","volume":"15","author":"Wu","year":"2003","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.jksuci.2018.02.015","article-title":"An intelligent approach to design of E-Commerce metasearch and ranking system using next-generation big data analytics","volume":"33","author":"Malhotra","year":"2021","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1016\/j.ins.2020.09.013","article-title":"Recommender systems based on generative adversarial networks: A problem-driven perspective","volume":"546","author":"Gao","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.eij.2015.06.005","article-title":"Recommendation systems: Principles, methods and evaluation","volume":"16","author":"Isinkaye","year":"2015","journal-title":"Egypt. Inform. J."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bai, Z., Huang, Y., Zhang, S., Li, P., Chang, Y., and Lin, X. (2022). Multi-Level Knowledge-Aware Contrastive Learning Network for Personalized Recipe Recommendation. Appl. Sci., 12.","DOI":"10.3390\/app122412863"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.neucom.2022.02.070","article-title":"FG-CF: Friends-aware graph collaborative filtering for POI recommendation","volume":"488","author":"Cai","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/s40537-022-00592-5","article-title":"A systematic review and research perspective on recommender systems","volume":"9","author":"Roy","year":"2022","journal-title":"J. Big Data"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.ejor.2017.07.005","article-title":"A framework for configuring collaborative filtering-based recommendations derived from purchase data","volume":"265","author":"Geuens","year":"2018","journal-title":"Eur. J. Oper. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.elerap.2018.01.012","article-title":"Recommendation system development for fashion retail e-commerce","volume":"28","author":"Hwangbo","year":"2018","journal-title":"Electron. Commer. Res. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.future.2021.08.017","article-title":"Human resource recommendation algorithm based on improved frequent itemset mining","volume":"126","author":"Liu","year":"2022","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9409","DOI":"10.1007\/s11063-023-11207-2","article-title":"Next-Cart Recommendation by Utilizing Personalized Item Frequency Information in Online Web Portals","volume":"55","author":"Sanjeev","year":"2023","journal-title":"Neural Process. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7095","DOI":"10.1007\/s11063-023-11252-x","article-title":"A collaborative filtering recommendation algorithm based on community detection and graph neural network","volume":"55","author":"Sheng","year":"2023","journal-title":"Neural Process. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Su, Y., Zhang, R., Erfani, S., and Xu, Z. (2021, January 18). Detecting beneficial feature interactions for recommender systems. Proceedings of the AAAI Conference on Artificial Intelligence, Online.","DOI":"10.1609\/aaai.v35i5.16561"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"103470","DOI":"10.1016\/j.ipm.2023.103470","article-title":"Efficient federated item similarity model for privacy-preserving recommendation","volume":"60","author":"Ding","year":"2023","journal-title":"Inf. Process. Manag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.procs.2020.03.047","article-title":"A multi-level tourism destination recommender system","volume":"170","author":"Alrasheed","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"28647","DOI":"10.1007\/s11042-021-10965-2","article-title":"A comprehensive analysis on movie recommendation system employing collaborative filtering","volume":"80","author":"Thakker","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7579","DOI":"10.1007\/s00500-023-08134-8","article-title":"Film and television art innovation in network environment by using collaborative filtering recommendation algorithm","volume":"27","author":"Lai","year":"2023","journal-title":"Soft Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3407190","article-title":"Recommender systems leveraging multimedia content","volume":"53","author":"Deldjoo","year":"2020","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1186\/s40537-021-00425-x","article-title":"User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system","volume":"8","author":"Widiyaningtyas","year":"2021","journal-title":"J. Big Data"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"75657","DOI":"10.1109\/ACCESS.2022.3192396","article-title":"An efficient approach for rational next-basket recommendation","volume":"10","author":"Fouad","year":"2022","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"012095","DOI":"10.1088\/1742-6596\/1437\/1\/012095","article-title":"Research on collaborative filtering recommendation algorithm based on mahout and user model","volume":"1437","author":"Song","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"40871","DOI":"10.1109\/ACCESS.2021.3065001","article-title":"Blockchain-assisted collaborative service recommendation scheme with data sharing","volume":"9","author":"Yan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"113347","DOI":"10.1016\/j.eswa.2020.113347","article-title":"Geographic-aware collaborative filtering for web service recommendation","volume":"151","author":"Botangen","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1007\/s11036-020-01673-6","article-title":"Personalized learning resource recommendation method based on dynamic collaborative filtering","volume":"26","author":"Wang","year":"2021","journal-title":"Mob. Netw. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"158","DOI":"10.3991\/ijet.v17i02.29013","article-title":"Collaborative filtering recommendation of online learning resources based on knowledge association model","volume":"17","author":"Jia","year":"2022","journal-title":"Int. J. Emerg. Technol. Learn. (IJET)"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. (2001, January 1\u20135). Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th International Conference on World Wide Web, Hong Kong, China.","DOI":"10.1145\/371920.372071"},{"key":"ref_43","unstructured":"(2025, January 25). MovieLens Dataset. Available online: http:\/\/grouplens.org\/datasets\/movielens\/."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/7\/294\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:12:59Z","timestamp":1760033579000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/7\/294"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,20]]},"references-count":43,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["computers14070294"],"URL":"https:\/\/doi.org\/10.3390\/computers14070294","relation":{},"ISSN":["2073-431X"],"issn-type":[{"type":"electronic","value":"2073-431X"}],"subject":[],"published":{"date-parts":[[2025,7,20]]}}}