Original Contribution: Stacked generalization
Neural Networks
Machine Learning
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Feature-based and Clique-based User Models for Movie Selection: A Comparative Study
User Modeling and User-Adapted Interaction
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Proceedings of the fourth ACM conference on Recommender systems
Context-aware movie recommendation based on signal processing and machine learning
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
Pareto-efficient hybridization for multi-objective recommender systems
Proceedings of the sixth ACM conference on Recommender systems
Improving quality control by early prediction of manufacturing outcomes
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Generating supplemental content information using virtual profiles
Proceedings of the 7th ACM conference on Recommender systems
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In this paper, we apply stacking, an ensemble learning method, to the problem of building hybrid recommendation systems. We also introduce the novel idea of using runtime metrics which represent properties of the input users/items as additional meta-features, allowing us to combine component recommendation engines at runtime based on user/item characteristics. In our system, component engines are level-1 predictors, and a level-2 predictor is learned to generate the final prediction of the hybrid system. The input features of the level-2 predictor are predictions from component engines and the runtime metrics. Experimental results show that our system outperforms each single component engine as well as a static hybrid system. Our method has the additional advantage of removing restrictions on component engines that can be employed; any engine applicable to the target recommendation task can be easily plugged into the system.