{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T21:52:59Z","timestamp":1792446779880,"version":"4.5.0"},"reference-count":115,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,7,22]],"date-time":"2020-07-22T00:00:00Z","timestamp":1595376000000},"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>Intelligence is the ability to learn from experience and use domain experts\u2019 knowledge to adapt to new situations. In this context, an intelligent Recommender System should be able to learn from domain experts\u2019 knowledge and experience, as it is vital to know the domain that the items will be recommended. Traditionally, Recommender Systems have been recognized as playlist generators for video\/music services (e.g., Netflix and Spotify), e-commerce product recommenders (e.g., Amazon and eBay), or social content recommenders (e.g., Facebook and Twitter). However, Recommender Systems in modern enterprises are highly data-\/knowledge-driven and may rely on users\u2019 cognitive aspects such as personality, behavior, and attitude. In this paper, we survey and summarize previously published studies on Recommender Systems to help readers understand our method\u2019s contributions to the field in this context. We discuss the current limitations of the state of the art approaches in Recommender Systems and the need for our new approach: A vision and a general framework for a new type of data-driven, knowledge-driven, and cognition-driven Recommender Systems, namely, Cognitive Recommender Systems. Cognitive Recommender Systems will be the new type of intelligent Recommender Systems that understand the user\u2019s preferences, detect changes in user preferences over time, predict user\u2019s unknown favorites, and explore adaptive mechanisms to enable intelligent actions within the compound and changing environments. We present a motivating scenario in banking and argue that existing Recommender Systems: (i) do not use domain experts\u2019 knowledge to adapt to new situations; (ii) may not be able to predict the ratings or preferences a customer would give to a product (e.g., loan, deposit, or trust service); and (iii) do not support data capture and analytics around customers\u2019 cognitive activities and use it to provide intelligent and time-aware recommendations.<\/jats:p>","DOI":"10.3390\/a13080176","type":"journal-article","created":{"date-parts":[[2020,7,22]],"date-time":"2020-07-22T05:10:30Z","timestamp":1595394630000},"page":"176","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":84,"title":["Towards Cognitive Recommender Systems"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5988-5494","authenticated-orcid":false,"given":"Amin","family":"Beheshti","sequence":"first","affiliation":[{"name":"Department of Computing, Macquarie University, Macquarie Park 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahpar","family":"Yakhchi","sequence":"additional","affiliation":[{"name":"Department of Computing, Macquarie University, Macquarie Park 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salman","family":"Mousaeirad","sequence":"additional","affiliation":[{"name":"Department of Computing, Macquarie University, Macquarie Park 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyed Mohssen","family":"Ghafari","sequence":"additional","affiliation":[{"name":"Department of Computing, Macquarie University, Macquarie Park 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Srinivasa Reddy","family":"Goluguri","sequence":"additional","affiliation":[{"name":"Department of Computing, Macquarie University, Macquarie Park 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Amin","family":"Edrisi","sequence":"additional","affiliation":[{"name":"Department of Computing, Macquarie University, Macquarie Park 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ricci, F., Rokach, L., and Shapira, B. 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