Resource Representative Model for Educational Resource Recommendation
Zhaoli Zhang, Di Zhang, Hai Liu, Xiaoxuan Shen · 2018
Educational resource recommendation has become an increasingly crucial problem. It is important for learners to find appropriate and high-quality educational resources in massively redundant information and educational resources of uneven quality. In this paper, a resource representative model based on user-resource network is proposed to select the representative resources from large user-resource network according to user's behaviors. The selected representative resources can provide coarse-grained resource recommendation for learning-beginners, and supply the resource pre-selection set for personalized recommendation of non-learning-beginners so as to offer better resources for personalized recommendation. The model defines the concept of resource representative degree and propose a method to calculate representative degree with considerations of relative influence, similarity and connectivity between resources. To optimize the proposed model effectively, we present a greedy heuristic algorithm with provable approximation guarantees on the public dataset "citeulike". Experimental results depict that proposed method significantly outperforms other traditional methods in representative resources selection.