{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T21:31:23Z","timestamp":1775079083378,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T00:00:00Z","timestamp":1679875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Recommender systems aim to forecast users\u2019 rank, interests, and preferences in specific products and recommend them to a user for purchase. Collaborative filtering is the most popular approach, where the user\u2019s past purchase behavior consists of the user\u2019s feedback. One of the most challenging problems in collaborative filtering is handling users whose previous item purchase behavior is unknown, (e.g., new users) or products for which user interactions are not available, (e.g., new products). In this work, we address the cold-start problem in recommender systems based on frequent patterns which are highly frequent in one set of users, but less frequent or infrequent in other sets of users. Such discriminant frequent patterns can distinguish one target set of users from all other sets. The proposed methodology, first forms different clusters of old users and then discovers discriminant frequent patterns for each different such cluster of users and finally exploits the latter to hallucinate the purchase behavior of new users. We also present empirical results to demonstrate the efficiency and accuracy of the proposed methodology.<\/jats:p>","DOI":"10.3390\/a16040182","type":"journal-article","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T07:32:09Z","timestamp":1679902329000},"page":"182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8161-647X","authenticated-orcid":false,"given":"Antiopi","family":"Panteli","sequence":"first","affiliation":[{"name":"Management Information Systems & Business Intelligence Laboratory, Department of Business Administration, University of Patras, GR 26504 Patras, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4468-702X","authenticated-orcid":false,"given":"Basilis","family":"Boutsinas","sequence":"additional","affiliation":[{"name":"Management Information Systems & Business Intelligence Laboratory, Department of Business Administration, University of Patras, GR 26504 Patras, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e12957","DOI":"10.2196\/12957","article-title":"The deep learning\u2013based recommender system \u201cPubmender\u201d for choosing a biomedical publication venue: Development and validation study","volume":"21","author":"Feng","year":"2019","journal-title":"J. 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