{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T07:26:27Z","timestamp":1774596387602,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T00:00:00Z","timestamp":1652054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The World Wide Web is evolving rapidly, and the Internet is now accessible to millions of users, providing them with the means to access a wealth of information, entertainment and e-commerce opportunities. Web browsing is largely impersonal and anonymous, and because of the large population that uses it, it is difficult to separate and categorize users according to their preferences. One solution to this problem is to create a web-platform that acts as a middleware between end users and the web, in order to analyze the data that is available to them. The method by which user information is collected and sorted according to preference is called \u2018user profiling\u2018. These profiles could be enriched using neural networks. In this article, we present our implementation of an online profiling mechanism in a virtual e-shop and how neural networks could be used to predict the characteristics of new users. The major contribution of this article is to outline the way our online profiles could be beneficial both to customers and stores. When shopping at a traditional physical store, real time targeted \u201cpersonalized\u201d advertisements can be delivered directly to the mobile devices of consumers while they are walking around the stores next to specific products, which match their buying habits.<\/jats:p>","DOI":"10.3390\/fi14050144","type":"journal-article","created":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T21:23:27Z","timestamp":1652131407000},"page":"144","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Adaptive User Profiling in E-Commerce and Administration of Public Services"],"prefix":"10.3390","volume":"14","author":[{"given":"Kleanthis G.","family":"Gatziolis","sequence":"first","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Peloponnese, 221 00 Tripoli, Greece"}]},{"given":"Nikolaos D.","family":"Tselikas","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Peloponnese, 221 00 Tripoli, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3656-277X","authenticated-orcid":false,"given":"Ioannis D.","family":"Moscholios","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, University of Peloponnese, 221 00 Tripoli, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,9]]},"reference":[{"key":"ref_1","first-page":"1953","article-title":"Enhanced web personalization for improved browsing experience","volume":"10","author":"Wagh","year":"2017","journal-title":"Adv. Comput. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Abri, S., Abri, R., and Cetin, S. (2020, January 18\u201320). A classification on different aspects of user modelling in personalized web search. Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval, Seoul, Korea.","DOI":"10.1145\/3443279.3443291"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bakaev, M.A., and Pogorelova, A.O. (2021, January 19\u201321). Profiling of Website Visitors Based on Dimensions of User Experience. Proceedings of the 2021 XV International Scientific-Technical Conference on Actual Problems Of Electronic Instrument Engineering (APEIE), Berdsk, Russia.","DOI":"10.1109\/APEIE52976.2021.9647688"},{"key":"ref_4","unstructured":"Kanoje, S., Girase, S., and Mukhopadhyay, D. (2015). User Profiling for Recommendation System. arXiv."},{"key":"ref_5","unstructured":"Kanoje, S., Girase, S., and Mukhopadhyay, D. (2015). User profiling trends, techniques and applications. arXiv."},{"key":"ref_6","first-page":"55","article-title":"A personalized requirement identifying model for design improvement based on user profiling","volume":"34","author":"Li","year":"2020","journal-title":"AI EDAM"},{"key":"ref_7","unstructured":"(2022, April 05). User Profile. Wikipedia, the Free Encyclopedia. Available online: https:\/\/en.wikipedia.org\/wiki\/User_profile."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Farid, M., Elgohary, R., Moawad, I., and Roushdy, M. (2018, January 10\u201312). User Profiling Approaches, Modeling, and Personalization. Proceedings of the 11th International Conference on Informatics & Systems (INFOS 2018), Cairo, Egypt. Available online: https:\/\/ssrn.com\/abstract=3389811.","DOI":"10.2139\/ssrn.3389811"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gu, Y., Ding, Z., Wang, S., and Yin, D. (2020, January 3\u20137). Hierarchical user profiling for e-commerce recommender systems. Proceedings of the 13th International Conference on Web Search and Data Mining, Houston, TX, USA.","DOI":"10.1145\/3336191.3371827"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e12471","DOI":"10.1111\/exsy.12471","article-title":"An efficient hybrid similarity measure based on user interests for recommender systems","volume":"37","author":"Hawashin","year":"2020","journal-title":"Expert Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhao, S., Li, S., Ramos, J., Luo, Z., Jiang, Z., Dey, A.K., and Pan, G. (2019). User profiling from their use of smartphone applications: A survey. Pervasive Mob. Comput., 59.","DOI":"10.1016\/j.pmcj.2019.101052"},{"key":"ref_12","unstructured":"Kyriazanos, D.M., Olesen, H., Hammersh\u00f8j, A.D., Heinze, E.K.S., Bessler, S., Zeiss, J., Patrikakis, C.Z., Nikolakopoulos, G., Amundsen, S., and Thuvesson, H. (2007). Specification of User Profile, Identity and Role Management for PNs and Integration to the PN Platform; IST Project MAGNET Beyond (My Personal Adaptive Global Net and Beyond) No. Deliverable D4.3.2 (D1.2.2) IST-027396, Aalborg Universitetsforlag."},{"key":"ref_13","unstructured":"Olesen, H., Noll, J., Hoffmann, M., Hammersh\u00f8j, A., Sapuppo, A., Iqbal, Z., Elahi, N., Chowdhury, M., Heikkinen, S., and Sutterer, M. (2022, April 05). User Profiles, Personalization and Privacy: WWRF Outlook Series. 2009; pp. 22\u201323. Available online: https:\/\/vbn.aau.dk\/en\/publications\/user-profiles-personalization-and-privacy-wwrf-outlook."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.clsr.2018.02.002","article-title":"Normative challenges of identification in the Internet of Things: Privacy, profiling, discrimination, and the GDPR","volume":"34","author":"Wachter","year":"2018","journal-title":"Comput. Law Secur. Rev."},{"key":"ref_15","unstructured":"Johnson, A., and Taatgen, N. (2005). User Modeling. Handbook of Human Factors in Web Design, Lawrence Erlbaum Associates."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106227","DOI":"10.1016\/j.knosys.2020.106227","article-title":"Mining user interest based on personality-aware hybrid filtering in social networks","volume":"206","author":"Dhelim","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_17","first-page":"661","article-title":"User interest model based on hybrid behaviors interest rate","volume":"3","author":"Xing","year":"2016","journal-title":"Appl. Res. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jnca.2016.06.012","article-title":"User profiling in intrusion detection: A review","volume":"72","author":"Peng","year":"2016","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_19","unstructured":"O\u2019Neil, C., and Schutt, R. (2014). Statistical Inference, Exploratory Data Analysis, and the Data Science Process, Doing Data Science. Doing Data Science, O\u2019Reilly Media, Inc.. Chapter 2."},{"key":"ref_20","unstructured":"Ad\u00e8r, H.J., Mellenbergh, G.J., and Hand, D.J. (2008). Phases and initial steps in data analysis. Advising on Research Methods: A Consultant\u2019s Companion, Johannes van Kessel Pub.. Chapter 14."},{"key":"ref_21","unstructured":"(2022, April 05). User Modeling. Wikipedia, the Free Encyclopedia. Available online: https:\/\/en.wikipedia.org\/wiki\/User_modeling."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1023\/A:1011145532042","article-title":"User Modeling in Human\u2013Computer Interaction","volume":"11","author":"Fischer","year":"2001","journal-title":"User Modeling User-Adapt. Interact."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"060017","DOI":"10.1063\/1.5043719","article-title":"Use of user modeling for personalization","volume":"Volume 1978","author":"Klubal","year":"2018","journal-title":"AIP Conference Proceedings"},{"key":"ref_24","first-page":"95","article-title":"Adaptive hypermedia","volume":"11","author":"Brusilovsky","year":"2001","journal-title":"User Modeling User-Adapt. Interact."},{"key":"ref_25","unstructured":"Abu-Naser, S.S., Alamawi, W.W., and Alfarra, M.F. (2022, April 05). Rule Based System for Diagnosing Wireless Connection Problems Using SL5 Object. Available online: https:\/\/philpapers.org\/go.pl?aid=ABURBS."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ricci, F., Rokach, L., Shapira, B., and Kantor, P. (2011). Introduction to Recommender Systems Handbook. Recommender Systems Handbook, Springer.","DOI":"10.1007\/978-0-387-85820-3"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Nielsen, J. (1994). Usability Engineering, Academic Press Inc.","DOI":"10.1016\/B978-0-08-052029-2.50007-3"},{"key":"ref_28","unstructured":"Clifton, C. (2022, April 05). Encyclopedia Britannica: Definition of Data Mining. Available online: http:\/\/www.britannica.com\/technology\/data-mining."},{"key":"ref_29","unstructured":"Ghorbani, A., and Zhang, J. (2007, January 14\u201317). GUMSAWS: A Generic User Modeling Server for Adaptive Web Systems. Proceedings of the Fifth Annual Conference on Communication Networks and Services Research (CNSR \u201807), Frederlcton, NB, USA."},{"key":"ref_30","unstructured":"McCarthy, K., Salam\u00f3, M., Coyle, L., McGinty, L., Smyth, B., and Nixon, P. (2006, January 11\u201313). Cats: A synchronous approach to collaborative group recommendation. Proceedings of the Florida Artificial Intelligence Research Society Conference (FLAIRS), Melbourne Beach, FL, USA. Available online: https:\/\/www.aaai.org\/Papers\/FLAIRS\/2006\/Flairs06-015.pdf."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Logesh, R., and Subramaniyaswamy, V. (2019). Exploring hybrid recommender systems for personalized travel applications. Cognitive Informatics and Soft Computing, Springer.","DOI":"10.1007\/978-981-13-0617-4_52"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"\u00c1lvarez M\u00e1rquez, J.O., and Ziegler, J. (2016). Hootle+: A group recommender system supporting preference negotiation. Cyted-Ritos International Workshop on Groupware, Springer.","DOI":"10.1007\/978-3-319-44799-5_12"},{"key":"ref_33","unstructured":"(2022, April 05). To Login or to Social Login. Available online: https:\/\/www.linkedin.com\/pulse\/login-social-scout-stevenson."},{"key":"ref_34","unstructured":"(2022, April 05). Directive 95\/46\/EC. Available online: https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=celex%3A31995L0046."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lopes, H., Pires, I.M., S\u00e1nchez San Blas, H., Garc\u00eda-Ovejero, R., and Leithardt, V. (2020). PriADA: Management and Adaptation of Information Based on Data Privacy in Public Environments. Computers, 9.","DOI":"10.3390\/computers9040077"},{"key":"ref_36","unstructured":"(2022, April 05). Trends in Online Shopping. A Global Nielsen Consumer Report. June 2010. Available online: https:\/\/www.nielsen.com\/wp-content\/uploads\/sites\/3\/2019\/04\/Q1-2010-GOS-Online-Shopping-Trends-June-2010.pdf."},{"key":"ref_37","unstructured":"(2022, April 05). Neural Network. Available online: https:\/\/en.wikipedia.org\/wiki\/Neural_network."},{"key":"ref_38","unstructured":"(2022, April 05). Neural Networks. Chapter 10. Available online: https:\/\/natureofcode.com\/book\/chapter-10-neural-networks."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gatziolis, K.G., and Boucouvalas, A.C. (2014, January 28\u201330). Discovering the impact of user profiling in e-services. Proceedings of the 2014 International Conference on Telecommunications and Multimedia (TEMU), Heraklion, Greece.","DOI":"10.1109\/TEMU.2014.6917762"},{"key":"ref_40","unstructured":"Chaudhuri, A., Samanta, D., and Sarma, M. (2021). Modeling user behaviour in research paper recommendation system. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1007\/s11036-018-1059-2","article-title":"Efficient user profiling based intelligent travel recommender system for individual and group of users","volume":"24","author":"Logesh","year":"2019","journal-title":"Mob. Netw. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s10844-021-00661-w","article-title":"User profiling and satisfaction inference in public information access services","volume":"58","author":"Flores","year":"2022","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kulkarni, T., Kabra, M., and Shankarmani, R. (2019, January 13\u201315). User Profiling Based Recommendation System for E-Learning. Proceedings of the 2019 IEEE 16th India Council International Conference (Indicon), Rajkot, India.","DOI":"10.1109\/INDICON47234.2019.9028982"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"144907","DOI":"10.1109\/ACCESS.2019.2944243","article-title":"A survey of user profiling: State-of-the-art, challenges, and solutions","volume":"7","author":"EEke","year":"2019","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Mamun, M., Al-Digeil, M., and Ahmed, S.S. (2021). Profiling Online Users: Emerging Approaches and Challenges. Securing Social Networks in Cyberspace, CRC Press.","DOI":"10.1201\/9781003134527-14"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Utami, E., Mihuandayani, M., Raharjo, S., Hartanto, A.D., and Adi, S. (2020, January 19\u201320). A Review on Social Media Based Profiling Analysis. Proceedings of the 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarang, Indonesia.","DOI":"10.1109\/iSemantic50169.2020.9234282"},{"key":"ref_47","first-page":"1","article-title":"Enhancing shopping experiences in smart retailing","volume":"12","author":"Bourg","year":"2021","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Aivalis, C.J., Gatziolis, K.G., and Boucouvalas, A.C. (2017). Innovations in E-Systems for E-Commerce. Innovations in E-Systems for Business and Commerce, Apple Academic Press.","DOI":"10.1201\/9781315207353-10"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Jung, S.G., An, J., Kwak, H., Salminen, J., and Jansen, B.J. (2018, January 25\u201328). Assessing the accuracy of four popular face recognition tools for inferring gender, age, and race. Proceedings of the Twelfth international AAAI conference on Web and Social Media, Palo Alto, CA, USA.","DOI":"10.1609\/icwsm.v12i1.15058"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Boucouvalas, A.C., Aivalis, C.J., and Gatziolis, K.G. (2015, January 3\u20135). Integrating retail and e-commerce using Web Analytics and intelligent sensors. Proceedings of the International Conference on E-Business and Telecommunications, Seoul, Korea.","DOI":"10.1007\/978-3-319-30222-5_1"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Aivalis, C.J., Gatziolis, K.G., and Boucouvalas, A.C. (2016, January 25\u201327). Evolving analytics for e-commerce applications: Utilizing big data and social media extensions. Proceedings of the 2016 International Conference on Telecommunications and Multimedia (TEMU), Heraklion, Greece.","DOI":"10.1109\/TEMU.2016.7551938"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chen, Y., He, J., Wei, W., Zhu, N., and Yu, C. (2021). A Multi-Model Approach for User Portrait. 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