Open AccessImproved hybrid recommendation with user similarity for adult learners
Author(s) -
Lai Ruilin,
Wang Tao,
Chen YanZhen
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.5353
Subject(s) - similarity (geometry) , computer science , online learning , hybrid learning , world wide web , learning community , recommender system , information overload , multimedia , mathematics education , artificial intelligence , psychology , image (mathematics)
Nowadays, adult learners like to study diverse and personal materials in e‐learning of continuing education. Even though some materials are conducted by fewer learners, they can still be propagated through the whole learning community. Meanwhile, the huge online materials cannot meet learners’ requirements. It leads to most learners encountering the e‐learning problems of ‘resource overload’ or ‘learning loses’, and learners giving up studying easily. So this study introduces an improved hybrid recommendation with user similarity (IHUS) for adult learners, which can generate the user's list of importance based on the greatest user similarity. In IHUS, when the online system runs from cold starting, the tags calculation is conducted. When this system achieves a stable learning community, an improved leaderRank method is conducted.