Browse Faculty and Staff Research - University Library by Subject "recommender systems"
Now showing items 1-3 of 3
Poster for: Approaches to systematic transaction data reuse: machine learning support of information discovery (F1000Research, 2019-05-13)The purpose of this poster is to detail approaches in systematic transactional data reuse using machine learning and network science visualization methods. Preliminary machine learning workflows were undertaken in WEKA and ...
(NISO, 2019-06-12)A personalized account-based recommender was developed in the University of Illinois Library's mobile app interface. The recommender system (RS) was derived from data mining topic clusters of items that are checked out ...
(ACRL, 2019-04-11)This research is focused on understanding user preferences for "my account"-based recommendations of library content. By interviewing users we have explored user attitudes about three areas of recommendation services; ...