{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T22:43:30Z","timestamp":1777761810873,"version":"3.51.4"},"reference-count":46,"publisher":"Emerald","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,7,24]]},"abstract":"<jats:p>Classic resource recommenders like Collaborative Filtering treat users as just another entity, thereby neglecting non-linear user-resource dynamics that shape attention and interpretation. SUSTAIN, as an unsupervised human category learning model, captures these dynamics. It aims to mimic a learner\u2019s categorization behavior. In this paper, we use three social bookmarking datasets gathered from BibSonomy, CiteULike and Delicious to investigate SUSTAIN as a user modeling approach to re-rank and enrich Collaborative Filtering following a hybrid recommender strategy. Evaluations against baseline algorithms in terms of recommender accuracy and computational complexity reveal encouraging results. Our approach substantially improves Collaborative Filtering and, depending on the dataset, successfully competes with a computationally much more expensive Matrix Factorization variant. In a further step, we explore SUSTAIN\u2019s dynamics in our specific learning task and show that both memorization of a user\u2019s history and clustering, contribute to the algorithm\u2019s performance. Finally, we observe that the users\u2019 attentional foci determined by SUSTAIN correlate with the users\u2019 level of curiosity, identified by the SPEAR algorithm. Overall, the results of our study show that SUSTAIN can be used to efficiently model attention-interpretation dynamics of users and can help improve Collaborative Filtering for resource recommendations.<\/jats:p>","DOI":"10.1561\/106.00000007","type":"journal-article","created":{"date-parts":[[2017,2,2]],"date-time":"2017-02-02T05:35:54Z","timestamp":1486013754000},"page":"45-61","source":"Crossref","is-referenced-by-count":8,"title":["Improving Collaborative Filtering Using a Cognitive Model of Human\n                    Category Learning"],"prefix":"10.1561","volume":"2","author":[{"given":"Simone","family":"Kopeinik","sequence":"first","affiliation":[{"name":"Knowledge Technologies Institute, Graz University of Technology , ,","place":["Graz, Austria"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dominik","family":"Kowald","sequence":"additional","affiliation":[{"name":"Knowledge Technologies Institute, Graz University of Technology , ,","place":["Graz, Austria"]},{"name":"Know-Center, Graz University of Technology , ,","place":["Graz, Austria"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ilire","family":"Hasani-Mavriqi","sequence":"additional","affiliation":[{"name":"Knowledge Technologies Institute, Graz University of Technology , ,","place":["Graz, Austria"]},{"name":"Know-Center, Graz University of Technology , ,","place":["Graz, Austria"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elisabeth","family":"Lex","sequence":"additional","affiliation":[{"name":"Knowledge Technologies Institute, Graz University of Technology , ,","place":["Graz, Austria"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2017,7,24]]},"reference":[{"key":"2026040606322908300_ref001","volume-title":"Proc. of RecSys\u201914","author":"Adamopoulos","year":"2014"},{"issue":"6","key":"2026040606322908300_ref002","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2005.99","article-title":"\u201cToward the Next Generation of\n                        Recommender Systems: A Survey of the State- of-the-Art and Possible\n                        Extensions\u201d","volume":"17","author":"Adomavicius","year":"2005","journal-title":"IEEE Transactions on Knowledge\n                        and Data Engineering"},{"key":"2026040606322908300_ref003","first-page":"1","volume-title":"Multiple Classifier Systems","author":"Bar","year":"2013"},{"key":"2026040606322908300_ref004","volume-title":"Proc. of\n                        ICML\u201904","author":"Basilico","year":"2004"},{"key":"2026040606322908300_ref005","first-page":"993","article-title":"\u201cLatent dirichlet\n                        allocation\u201d","volume":"3","author":"Blei","year":"2003","journal-title":"The Journal of machine Learning\n                        research"},{"key":"2026040606322908300_ref006","volume-title":"Proc. of\n                        RecSys\u201911","author":"Cantador","year":"2011"},{"issue":"3","key":"2026040606322908300_ref007","doi-asserted-by":"crossref","first-page":"17:1","DOI":"10.1145\/2533670.2533675","volume":"3","author":"Chen","year":"2013","journal-title":"ACM Trans. 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