Community Collaborative Filtering for E-Learning
Jian Hu, Wei Zhang · 2008
Recommender systems for e-learning need to consider the specific demands and requirements and to improve the 'educational aspects' for the learners. In this paper, we present a novel hybrid recommender system from a perspective of considering learner community structures to collaborative filtering. In our approach, multiple types of information are explored and exploited, including learners and learning items and learner social information. Leveraging the types of information, we apply multiple techniques from data mining, including multi-relational data mining and graph data mining, to explicitly discovery learner community structures, which in turn are used in collaborative filtering. Our experiments suggest that our approach provides improved accurate recommendations than other approaches.