{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:07:51Z","timestamp":1774368471287,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T00:00:00Z","timestamp":1645488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003447","name":"State Scholarships Foundation","doi-asserted-by":"publisher","award":["2019-050-0503-18772"],"award-info":[{"award-number":["2019-050-0503-18772"]}],"id":[{"id":"10.13039\/501100003447","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>A domain that has gained popularity in the past few years is personalized advertisement. Researchers and developers collect user contextual attributes (e.g., location, time, history, etc.) and apply state-of-the-art algorithms to present relevant ads. A problem occurs when the user has limited or no data available and, therefore, the algorithms cannot work well. This situation is widely referred in the literature as the \u2018cold-start\u2019 case. The aim of this manuscript is to explore this problem and present a prediction approach for personalized mobile advertising systems that addresses the cold-start, and especially the frozen user case, when a user has no data at all. The approach consists of three steps: (a) identify existing datasets and use specific attributes that could be gathered from a frozen user, (b) train and test machine learning models in the existing datasets and predict click-through rate, and (c) the development phase and the usage in a system.<\/jats:p>","DOI":"10.3390\/a15030072","type":"journal-article","created":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T22:35:09Z","timestamp":1645569309000},"page":"72","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Machine Learning Approach for Solving the Frozen User Cold-Start Problem in Personalized Mobile Advertising Systems"],"prefix":"10.3390","volume":"15","author":[{"given":"Iosif","family":"Viktoratos","sequence":"first","affiliation":[{"name":"Department of Economics, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece"}]},{"given":"Athanasios","family":"Tsadiras","sequence":"additional","affiliation":[{"name":"Department of Economics, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rula, J.P., Jun, B., and Bustamante, F.E. 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