Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Guest Editors' Introduction: Emerging Internet Technologies for E-Learning
IEEE Internet Computing
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
A test-sheet-generating algorithm for multiple assessment requirements
IEEE Transactions on Education
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Question calibration especially on difficulty degree is important for supporting Web-based testing and assessment. Currently, Item Response Theory (IRT) has traditionally applied for question difficulty calibration. However, it is tedious and time-consuming to collect sufficient historical response information manually, and computational expensive to calibrate question difficulty especially for large-scale question datasets. In this paper, we propose an effective Content-based Collaborative Filtering (CCF) approach for automatic calibration of question difficulty degree. In the proposed approach, a dataset of questions with available user responses and knowledge features is first gathered. Next, collaborative filtering is used to predict unknown user responses from known responses of questions. With all the responses, the difficulty degree of each question in the dataset is then estimated using IRT. Finally, when a new question is queried, the content-based similarity approach is used to find similar existing questions from the dataset based on the knowledge features for estimating the new question's difficulty degree. In this paper, we will present the proposed CCF approach and its performance evaluation in comparison with other techniques.