Volume 5, Issue 3 (9-2013)                   IJICTR 2013, 5(3): 25-33 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Salehi M, Nakhai Kamalabadi I, Ghaznavi Ghoushchi M B. A New Adaptive Hybrid Recommender Framework for Learning Material Recommendation . IJICTR. 2013; 5 (3) :25-33
URL: http://ijict.itrc.ac.ir/article-1-150-en.html
1- Department of Industrial Engineering Faculty of Engineering, Tarbiat Modares University Tehran, Iran
2- Department of Electrical Engineering Faculty of Engineering, Shahed University Tehran, Iran
Abstract:   (2582 Views)

Recommender system is a promising technology in online learning environments to present personalized offers for supporting activity of users. According to difficulty of locating appropriate learning materials to learners, this paper proposes an adaptive hybrid recommender framework that considers dynamic interests of learners and multi-attribute of materials in the unified model. Since learners express their preference based on some specific attributes of materials, learner preference matrix (LPM) is introduced that can model the interest of learners based on attributes of materials using historical rating of accessed materials by learners. Then, the approach uses collaborative filtering and content based filtering to generate hybrid recommendation. In addition, a new adaptive strategy is used to model dynamic preference of learners. The experiments show that our proposed method outperforms the previous algorithms on precision, recall and intra-list similarity measure and also can alleviate the sparsity problem.

Full-Text [PDF 2457 kb]   (1057 Downloads)    
Type of Study: Research | Subject: Information Technology

Add your comments about this article : Your username or Email: