A Hybrid Attribute–based Recommender System for E–learning Material Recommendation

Recommendation system is a significant part of e–learning systems for personalization and recommendation of appropriate materials to the learner. However, in the existing recommendation algorithms, attributes of materials that can improve the quality of recommendation are not fully considered. For addressing this problem, a new material recommendation approach is proposed based on modeling of materials in a multidimensional space of material's attribute. Herein, each learner is modeled by a matrix that can take into account multi–attribute of materials. The recommender is adaptive to individual learner's preference as well as one's changing interest. Recommendation is generated by content–based filtering, collaborative filtering and some hybrid approaches. In attribute–based approach, the learner's real learning preference can be satisfied accurately according to the real–time up dated contextual information. The main contribution of this paper is modelling of learning material attributes in an effective recommendation framework