Improved hybrid recommendation with user similarity for adult learners

Abstract

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.

Keywords

• internet
• learning (artificial intelligence)
• computer aided instruction
• study diverse materials
• personal materials
• fewer learners
• huge online materials
• e-learning problems
• learning loses
• improved hybrid recommendation
• adult learners
• greatest user similarity
• stable learning community