Original Contribution: Stacked generalization
Neural Networks
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Machine Learning
Method combination for document filtering
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
Automatic personalization based on Web usage mining
Communications of the ACM
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Machine Learning
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
An empirical evaluation of classifier combination schemes for predicting user navigational behavior
ITCC '03 Proceedings of the International Conference on Information Technology: Computers and Communications
Information retrieval: a framework for recommending text-based classification algorithms
Information retrieval: a framework for recommending text-based classification algorithms
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Stacked generalization: when does it work?
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Learning to integrate web taxonomies
Web Semantics: Science, Services and Agents on the World Wide Web
Using genetic algorithms for data mining optimization in an educational web-based system
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Web mining in soft computing framework: relevance, state of the art and future directions
IEEE Transactions on Neural Networks
International Journal of Computer Applications in Technology
Improving the prediction accuracy of liver disorder disease with oversampling
AMERICAN-MATH'12/CEA'12 Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
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An essential goal of the present web engineering is the development of efficient and competitive applications. This objective can be achieved by building recommender systems endowed with suitable web mining algorithms. Multiclassifiers are reliable data mining models that have been hardly used in the web system area. The paper presents a comparative study among different simple classifiers and multiclassifiers using a dataset from MovieLens recommender system. The aim of the work is to identify when the use of multiclassifiers in this type of systems is efficient.