Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A novel approach for assisting teachers in analyzing student web-searching behaviors
Computers & Education
TSCAN: a novel method for topic summarization and content anatomy
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On social networks and collaborative recommendation
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TagiCoFi: tag informed collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Document recommendation in social tagging services
Proceedings of the 19th international conference on World wide web
FacetCube: a framework of incorporating prior knowledge into non-negative tensor factorization
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Survey on social tagging techniques
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Using complex network features for fast clustering in the web
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Personalized PageRank vectors for tag recommendations: inside FolkRank
Proceedings of the fifth ACM conference on Recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Improving Search by Extending Tags According to Recommendation Level and Combinations of Types
SKG '11 Proceedings of the 2011 Seventh International Conference on Semantics, Knowledge and Grids
Web-based interaction: A review of three important human factors
International Journal of Information Management: The Journal for Information Professionals
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Prior knowledge is an important issue in the study of concept acquisition among students. Traditional studies on prior knowledge generation during reading activities have focused on extracting sentences from reading materials that are manually generated by website administrators and educators. This is time-consuming and strenuous, and hence personalized prior knowledge recommendation is difficult to perform. To cope with this problem, we combine the concept of prior knowledge with social tagging methods to assist the reading comprehension of students studying English. We incorporate tags into a tag based learning approach, which then identifies suitable supplementary materials for quickly constructing a student's prior knowledge reservoir. The experimental results demonstrate that the proposed approach benefits the students by embedding the additional information in social knowledge, and hence significantly improve their on-line reading efficiency.