Efficient personalized e - learning material recommender systems based on incremental frequent pattern mining
نویسنده
چکیده
Personalized e-learning material recommenders are known for discovering associations between learner's requirements and learning materials. They usually use association rule mining in which the most time-consuming part is frequent pattern mining from log files. Since the content of log files and learner profiles are frequently changed, frequent patterns must be updated to discover valid association rules. Obviously, updating frequent patterns by rerunning the mining process from scratch can be very time-consuming. In this paper, firstly we propose a general architecture for developing efficient personalized learning recommender systems using incremental association rule mining. Consequently, a new method is proposed for incremental frequent pattern mining from log files, which is the most computationally-intensive process in the proposed architecture. The content of log file is captured by using a well-organized tree in one database scan. While the log files are changed the tree can be incrementally updated. The experimental results show that using the proposed method enhances the efficiency of personalized e-learning material recommenders.
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تاریخ انتشار 2011