Career Goal-based E-Learning Recommendation Using Enhanced Collaborative Filtering and PrefixSpan

• DOI: 10.4018/ijmbl.2018070103
• Publication Date: 2018-05-03T16:32:58Z
• Authors: Xueying Ma; Lu Ye
• Keywords: Relevance; Cold start (automotive); Personalized Learning

This article describes how e-learning recommender systems nowadays have applied different kinds of techniques to recommend personalized learning content for users based on their preference, goals, interests and background information. However, the cold-start problem which exists in traditional recommendation algorithms are still left over a few them seriously affected goals users. Thus, an intelligent system been developed can professional targeted courses according career goals. First, enhanced collaborative filtering (CF) approach is proposed considering users' Then, relevance between calculated alleviate specialized Finally, PrefixSpan algorithm combined with above methods generate path step by step. Some experiments carried out real professions test performance hybrid algorithm.