Approximately matching context-free languages
Information Processing Letters
Personalized, interactive news on the Web
Multimedia Systems
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
New algorithm for ordered tree-to-tree correction problem
Journal of Algorithms
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Efficient and Scalable Algorithm for Clustering XML Documents by Structure
IEEE Transactions on Knowledge and Data Engineering
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Finding an optimum edit script between an XML document and a DTD
Proceedings of the 2005 ACM symposium on Applied computing
Approximate XML document matching
Proceedings of the 2005 ACM symposium on Applied computing
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Internet users are becoming overwhelmed by rapidly growing Web information. Two commonly used technologies to solve the problem are information retrieval and collaborative filtering. Existing information retrieval methods have been mainly developed for handling flat documents and current collaborative filtering systems still suffer from the sparsity problem in which most users may rate very few items comparing with the large number of available items in the systems. Moreover, current methods usually cope with these issues separately. In this paper, we develop an intelligent agent framework that integrates document collection, information retrieval and recommendation. In order to improve the query performance, similar XML documents are grouped together based on structural information. Different with conventional methods, we use tree-edit distance to measure the similarity/dissimilarity among XML documents. In collaborative filtering, we convert the recommendation problem into a classification problem and solve it by multi-class support vector machines. Experimental studies show that the accurate rates of our recommendation method outperform existing ones.