{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T17:50:05Z","timestamp":1777917005462,"version":"3.51.4"},"reference-count":52,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,5,9]],"date-time":"2018-05-09T00:00:00Z","timestamp":1525824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Recommender systems are powerful online tools that help to overcome problems of information overload. They make personalized recommendations to online users using various data mining and filtering techniques. However, most of the existing recommender systems use a single rating to represent the preference of user on an item. These techniques have several limitations as the preference of the user towards items may depend on several attributes of the items. Multi-criteria recommender systems extend the single rating recommendation techniques to incorporate multiple criteria ratings for improving recommendation accuracy. However, modeling the criteria ratings in multi-criteria recommender systems to determine the overall preferences of users has been considered as one of the major challenges in multi-criteria recommender systems. In other words, how to additionally take the multi-criteria rating information into account during the recommendation process is one of the problems of multi-criteria recommender systems. This article presents a methodological framework that trains artificial neural networks with particle swarm optimization algorithms and uses the neural networks for integrating the multi-criteria rating information and determining the preferences of users. The proposed neural network-based multi-criteria recommender system is integrated with k-nearest neighborhood collaborative filtering for predicting unknown criteria ratings. The proposed approach has been tested with a multi-criteria dataset for recommending movies to users. The empirical results of the study show that the proposed model has a higher prediction accuracy than the corresponding traditional recommendation technique and other multi-criteria recommender systems.<\/jats:p>","DOI":"10.3390\/informatics5020025","type":"journal-article","created":{"date-parts":[[2018,5,10]],"date-time":"2018-05-10T03:48:27Z","timestamp":1525924107000},"page":"25","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Artificial Neural Networks and Particle Swarm Optimization Algorithms for Preference Prediction in Multi-Criteria Recommender Systems"],"prefix":"10.3390","volume":"5","author":[{"given":"Mohamed","family":"Hamada","sequence":"first","affiliation":[{"name":"Software Engineering Lab, University of Aizu, Aizu-Wakamatsu 965-8580, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed","family":"Hassan","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Bayero University Kano, Kano, P.M.B. 3011, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.knosys.2011.07.021","article-title":"A collaborative filtering approach to mitigate the new user cold start problem","volume":"26","author":"Bobadilla","year":"2012","journal-title":"Knowl.-Based Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hassan, M., and Hamada, M. (2016, January 25\u201327). Recommending Learning Peers for Collaborative Learning through Social Network Sites. Proceedings of the 2016 7th IEEE International Conference on Intelligent Systems, Modelling and Simulation (ISMS), Bangkok, Thailand.","DOI":"10.1109\/ISMS.2016.22"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.dss.2015.03.008","article-title":"Recommender system application developments: A survey","volume":"74","author":"Lu","year":"2015","journal-title":"Decis. Support Syst."},{"key":"ref_4","first-page":"409","article-title":"Performance Comparison of Featured Neural Network Trained with Backpropagation and Delta Rule Techniques for Movie Rating Prediction in Multi-criteria Recommender Systems","volume":"40","author":"Hassan","year":"2016","journal-title":"Informatica"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Adomavicius, G., and Kwon, Y. (2011). Multi-criteria recommender systems. Recommender Systems Handbook, Springer.","DOI":"10.1007\/978-0-387-85820-3_24"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11125","DOI":"10.1016\/j.amc.2012.04.069","article-title":"Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm","volume":"218","author":"Mirjalili","year":"2012","journal-title":"Appl. Math. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hassan, M., and Hamada, M. (2017). Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems. Computation, 5.","DOI":"10.3390\/computation5030040"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1080\/10798587.2000.10642829","article-title":"Genetic algorithm solution of the TSP avoiding special crossover and mutation","volume":"8","year":"2002","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.asoc.2017.04.014","article-title":"Forecasting financial time series volatility using Particle Swarm Optimization trained Quantile Regression Neural Network","volume":"58","author":"Pradeepkumar","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1080\/10798587.2015.1095484","article-title":"A Ranging Model Based on BP Neural Network","volume":"22","author":"Chen","year":"2016","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1080\/10798587.2014.962239","article-title":"A hybrid evolutionary algorithm for numerical optimization problem","volume":"21","author":"Xue","year":"2015","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1016\/j.amc.2006.07.025","article-title":"A hybrid particle swarm optimization\u2013back-propagation algorithm for feedforward neural network training","volume":"185","author":"Zhang","year":"2007","journal-title":"Appl. Math. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Du, K.L., and Swamy, M. (2016). Particle swarm optimization. Search and Optimization by Metaheuristics, Springer.","DOI":"10.1007\/978-3-319-41192-7"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Settles, M., Rodebaugh, B., and Soule, T. (2003). Comparison of genetic algorithm and particle swarm optimizer when evolving a recurrent neural network. Genetic and Evolutionary Computation\u2014GECCO 2003, Springer.","DOI":"10.1007\/3-540-45105-6_17"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Adomavicius, G., Manouselis, N., and Kwon, Y. (2015). Multi-criteria recommender systems. Recommender Systems Handbook, Springer.","DOI":"10.1007\/978-1-4899-7637-6_25"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hill, W., Stead, L., Rosenstein, M., and Furnas, G. (1995, January 7\u201311). Recommending and evaluating choices in a virtual community of use. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Denver, CO, USA.","DOI":"10.1145\/223904.223929"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. (1994, January 22\u201326). GroupLens: An open architecture for collaborative filtering of netnews. Proceedings of the 1994 ACM conference on Computer supported cooperative work, Chapel Hill, NC, USA.","DOI":"10.1145\/192844.192905"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shardanand, U., and Maes, P. (1995, January 7\u201311). Social information filtering: Algorithms for automating \u201cword of mouth\u201d. Proceedings of the SIGCHI conference on Human factors in computing systems, Denver, CO, USA.","DOI":"10.1145\/223904.223931"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Planti\u00e9, M., Montmain, J., and Dray, G. (2005). Movies recommenders systems: Automation of the information and evaluation phases in a multi-criteria decision-making process. Database and Expert Systems Applications, Springer.","DOI":"10.1007\/11546924_62"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bilge, A., and Kaleli, C. (2014, January 14\u201316). A multi-criteria item-based collaborative filtering framework. Proceedings of the 2014 11th IEEE International Joint Conference on Computer Science and Software Engineering (JCSSE), Chon Buri, Thailand.","DOI":"10.1109\/JCSSE.2014.6841835"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Adomavicius, G., and Kwon, Y. (2007). New recommendation techniques for multicriteria rating systems. IEEE Intell. Syst., 22.","DOI":"10.1109\/MIS.2007.58"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sanchez-Vilas, F., Ismoilov, J., Lousame, F.P., Sanchez, E., and Lama, M. (2011, January 22\u201327). Applying multicriteria algorithms to restaurant recommendation. Proceedings of the 2011 IEEE\/WIC\/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, IEEE Computer Society, Lyon, France.","DOI":"10.1109\/WI-IAT.2011.124"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lousame, F.P., and S\u00e1nchez, E. (2009, January 23\u201325). View-based recommender systems. Proceedings of the Third ACM Conference on Recommender Systems, New York, NY, USA.","DOI":"10.1145\/1639714.1639795"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fu, Y., Liu, B., Ge, Y., Yao, Z., and Xiong, H. (2014, January 24\u201326). User preference learning with multiple information fusion for restaurant recommendation. Proceedings of the 2014 SIAM International Conference on Data Mining, Philadelphia, PA, USA.","DOI":"10.1137\/1.9781611973440.54"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fang, Y., and Si, L. (2011, January 23\u201327). Matrix co-factorization for recommendation with rich side information and implicit feedback. Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, Chicago, IL, USA.","DOI":"10.1145\/2039320.2039330"},{"key":"ref_27","unstructured":"Cheng, C., Yang, H., King, I., and Lyu, M.R. (2012, January 22\u201326). Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks. Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI\u201912), Toronto, ON, Canada."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.elerap.2015.08.004","article-title":"A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA\u2013ANFIS","volume":"14","author":"Nilashi","year":"2015","journal-title":"Electr. Commer. Res. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Reynolds, D. (2015). Gaussian mixture models. Encyclopedia of Biometrics, Springer.","DOI":"10.1007\/978-1-4899-7488-4_196"},{"key":"ref_30","first-page":"19","article-title":"A multi-criteria recommender system for tourism using fuzzy approach","volume":"3","author":"Farokhi","year":"2016","journal-title":"J. Soft Comput. Decis. Support Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJDSST.2017100101","article-title":"A Recommender System Based on Multi-Criteria Aggregation","volume":"9","author":"Fomba","year":"2017","journal-title":"Int. J. Decis. Support Syst. Technol. (IJDSST)"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1016\/j.ins.2009.11.011","article-title":"Choquet integrals of weighted intuitionistic fuzzy information","volume":"180","author":"Xu","year":"2010","journal-title":"Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/MIS.2011.33","article-title":"Multicriteria user modeling in recommender systems","volume":"26","author":"Lakiotaki","year":"2011","journal-title":"IEEE Intell. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Choudhary, P., Kant, V., and Dwivedi, P. (2017, January 24\u201326). A Particle Swarm Optimization Approach to Multi Criteria Recommender System Utilizing Effective Similarity Measures. Proceedings of the 9th International Conference on Machine Learning and Computing, Singapore.","DOI":"10.1145\/3055635.3056619"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jannach, D., Karakaya, Z., and Gedikli, F. (2012, January 4\u20138). Accuracy improvements for multi-criteria recommender systems. Proceedings of the 13th ACM Conference on Electronic Commerce, Valencia, Spain.","DOI":"10.1145\/2229012.2229065"},{"key":"ref_36","first-page":"2079","article-title":"On over-fitting in model selection and subsequent selection bias in performance evaluation","volume":"11","author":"Cawley","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hassan, M., and Hamada, M. (2016, January 7\u20139). Enhancing learning objects recommendation using multi-criteria recommender systems. Proceedings of the 2016 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), Bangkok, Thailand.","DOI":"10.1109\/TALE.2016.7851771"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hassan, M., and Hamada, M. (2017, January 10\u201312). Smart media-based context-aware recommender systems for learning: A conceptual framework. Proceedings of the 2017 16th International Conference on Information Technology Based Higher Education and Training (ITHET), Ohrid, Macedonia.","DOI":"10.1109\/ITHET.2017.8067805"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"776","DOI":"10.2991\/ijcis.2017.10.1.52","article-title":"Fuzzy tools in recommender systems: A survey","volume":"10","author":"Yera","year":"2017","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.knosys.2013.03.012","article-title":"Recommender systems survey","volume":"46","author":"Bobadilla","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"14609","DOI":"10.1016\/j.eswa.2011.05.021","article-title":"A framework for collaborative filtering recommender systems","volume":"38","author":"Bobadilla","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hassan, M., and Hamada, M. (2017). A Neural Networks Approach for Improving the Accuracy of Multi-Criteria Recommender Systems. Appl. Sci., 7.","DOI":"10.3390\/app7090868"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kennedy, J. (2011). Particle swarm optimization. Encyclopedia of Machine Learning, Springer.","DOI":"10.1007\/978-0-387-30164-8_630"},{"key":"ref_44","first-page":"340","article-title":"A hybrid swarm optimization approach for feature set reduction in digital mammograms","volume":"9","author":"Jona","year":"2012","journal-title":"WSEAS Trans. Inf. Sci. Appl."},{"key":"ref_45","unstructured":"Hu, X., Eberhart, R.C., and Shi, Y. (2003, January 26). Particle swarm with extended memory for multiobjective optimization. Proceedings of the 2003 IEEE, Swarm Intelligence Symposium, 2003 (SIS\u201903), Indianapolis, IN, USA."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1039\/C4MB00414K","article-title":"PLS\/OPLS models in metabolomics: The impact of permutation of dataset rows on the K-fold cross-validation quality parameters","volume":"11","author":"Triba","year":"2015","journal-title":"Mol. BioSyst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.renene.2017.08.061","article-title":"A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)","volume":"115","author":"Rohani","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.ejso.2014.01.014","article-title":"Cross-validation of three predictive tools for non-sentinel node metastases in breast cancer patients with micrometastases or isolated tumor cells in the sentinel node","volume":"40","author":"Tvedskov","year":"2014","journal-title":"Eur. J. Surg. Oncol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.jmsy.2013.05.006","article-title":"Feature selection for manufacturing process monitoring using cross-validation","volume":"32","author":"Shao","year":"2013","journal-title":"J. Manuf. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.neucom.2015.08.118","article-title":"Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation","volume":"198","author":"Jiang","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Jannach, D., Lerche, L., Gedikli, F., and Bonnin, G. (2013). What recommenders recommend\u2013an analysis of accuracy, popularity, and sales diversity effects. International Conference on User Modeling, Adaptation, and Personalization, Springer.","DOI":"10.1007\/978-3-642-38844-6_3"},{"key":"ref_52","unstructured":"Owen, S., Anil, R., Dunning, T., and Friedman, E. (2011). Mahout in Action, Manning Publications Co."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/5\/2\/25\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:03:55Z","timestamp":1760195035000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/5\/2\/25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,9]]},"references-count":52,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["informatics5020025"],"URL":"https:\/\/doi.org\/10.3390\/informatics5020025","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,9]]}}}