{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:53:44Z","timestamp":1761126824676,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Saud University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>In the recommendation system (RS) literature, a distinction exists between studies dedicated to fully operational (known users\/items) and cold-start (new users\/items) RSs. The warm-up phase\u2014the transition between the two\u2014is not widely researched, despite evidence that attrition rates are the highest for users and content providers during such periods. RS formulations, particularly deep learning models, do not easily allow for a warm-up phase. Herein, we propose two independent and complementary models to increase RS performance during the warm-up phase. The models apply to any cold-start RS expressible as a function of all user features, item features, and existing users\u2019 preferences for existing items. We demonstrate substantial improvements: Accuracy-oriented metrics improved by up to 14% compared with not handling warm-up explicitly. Non-accuracy-oriented metrics, including serendipity and fairness, improved by up to 12% compared with not handling warm-up explicitly. The improvements were independent of the cold-start RS algorithm. Additionally, this paper introduces a method of examining the performance metrics of an RS during the warm-up phase as a function of the number of user\u2013item interactions. We discuss problems such as data leakage and temporal consistencies of training\/testing\u2014often neglected during the offline evaluation of RSs.<\/jats:p>","DOI":"10.3390\/computers14080302","type":"journal-article","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T08:13:31Z","timestamp":1753431211000},"page":"302","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Novel Models for the Warm-Up Phase of Recommendation Systems"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1062-9001","authenticated-orcid":false,"given":"Nourah","family":"AlRossais","sequence":"first","affiliation":[{"name":"Information Technology (IT) Department, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Ko, H., Lee, S., Park, Y., and Choi, A. (2022). A survey of recommendation systems: Recommendation models, techniques, and application fields. Electronics, 11.","DOI":"10.3390\/electronics11010141"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.3233\/IDA-163209","article-title":"Hybrid recommender systems: A systematic literature review","volume":"21","author":"Morisio","year":"2017","journal-title":"Intell. 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