Personalized recommendation of learning material using sequential pattern mining and attribute based collaborative filtering
Material recommender system is a significant part of e-learning systems for personalization and recommendation of appropriate materials to learners. However, in the existing recommendation algorithms, dynamic interests and multi-preference of learners and multidimensional-attribute of materials are not fully considered simultaneously. Moreover, these algorithms cannot effectively use the learners historical sequential patterns of material accessing in recommendation. For addressing these problems and improving the accuracy and quality of recommendation, a new material recommender system framework based on sequential pattern mining and multidimensional attribute-based collaborative filtering (CF) is proposed. In the sequential pattern based approach, modified Apriori and PrefixSpan algorithms are implemented to discover latent patterns in accessing of materials and use them for recommendation. Leaner Preference Tree (LPT) is introduced to take into account multidimensional-attribute of materials, and learners rating and model dynamic and multi-preference of learners in the multidimensional attribute-based CF approach. Finally, the recommendation results of two approaches are combined using cascade, weighted and mixed methods. The proposed method outperforms the previous algorithms on the classification accuracy measures and the learners real learning preference can be satisfied accurately according to the real-time up dated contextual information.
personalized recommendation learning material e-learning apriori algorithm dynamic preference multi-attribute