{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T20:54:16Z","timestamp":1689108856612},"reference-count":17,"publisher":"World Scientific Pub Co Pte Lt","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2017,12]]},"abstract":"<jats:p> Collaborative filtering methods are widely accepted and used for item recommendation in various applications and domains. Their simplicity and ability to provide recommendations without the need for specific domain knowledge makes them utilized in both academic and industrial community. However, continuous dynamics of the system makes them vulnerable on different kind of changes, such as the change in user\u2019s preferences or the appearance of new users and items in the system. It is particularly emphasized in user-based collaborative filtering recommendations which are based on both users and its nearest neighbor\u2019s long-term profiles. In this work an approach is presented in order to recognize some of the changes and provide an upgraded model that provides improved performance. Such changes include deviations in user\u2019s mean ratings, changes in neighbors\u2019 similarities, as well as deviations from neighbors\u2019 mean rating. For each of the proposed improvements a set of matching parameters are identified that form a long-term user\u2019s profile. The performance of the proposed models compared to standard user-based collaborative filtering methods is evaluated in terms of prediction accuracy. The experimental results performed on real data set show sound improvement utilizing adjustment of deviations from user\u2019s mean ratings and the neighbor\u2019s similarities, as well as promising improvement with adjustment of neighbor\u2019s mean ratings. <\/jats:p>","DOI":"10.1142\/s021821301750021x","type":"journal-article","created":{"date-parts":[[2017,9,18]],"date-time":"2017-09-18T06:02:12Z","timestamp":1505714532000},"page":"1750021","source":"Crossref","is-referenced-by-count":1,"title":["Modeling Long-Term User Profile in Collaborative Filtering"],"prefix":"10.1142","volume":"26","author":[{"given":"Bakir","family":"Karahod\u017ea","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering, University in Sarajevo, Sarajevo, 71000, Bosnia and Herzegovina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D\u017eenana","family":"\u00d0onko","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, University in Sarajevo, Sarajevo, 71000, Bosnia and Herzegovina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haris","family":"\u0160upi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, University in Sarajevo, Sarajevo, 71000, Bosnia and Herzegovina"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2017,12,12]]},"reference":[{"key":"p_1","doi-asserted-by":"publisher","DOI":"10.1145\/1055709.1055714"},{"key":"p_3","doi-asserted-by":"publisher","DOI":"10.1561\/1100000009"},{"key":"p_4","doi-asserted-by":"publisher","DOI":"10.1016\/j.fss.2008.03.017"},{"key":"p_5","doi-asserted-by":"publisher","DOI":"10.1145\/1721654.1721677"},{"key":"p_7","doi-asserted-by":"publisher","DOI":"10.1023\/A:1020443909834"},{"key":"p_8","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.7"},{"key":"p_9","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2005.06.010"},{"key":"p_10","first-page":"22","author":"Ziegler C. 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