A New Adaptive Hybrid Recommender Framework for Learning Material Recommendation

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.