Original Contribution: Stacked generalization
Neural Networks
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Computer
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Anchor space for classification and similarity measurement of music
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Improving multilabel analysis of music titles: a large-scale validation of the correction approach
IEEE Transactions on Audio, Speech, and Language Processing
Semantic Annotation and Retrieval of Music and Sound Effects
IEEE Transactions on Audio, Speech, and Language Processing
Large-scale music tag recommendation with explicit multiple attributes
Proceedings of the international conference on Multimedia
Audio tag annotation and retrieval using tag count information
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Enhancing multi-label music genre classification through ensemble techniques
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A Probabilistic Model to Combine Tags and Acoustic Similarity for Music Retrieval
ACM Transactions on Information Systems (TOIS)
Music retagging using label propagation and robust principal component analysis
Proceedings of the 21st international conference companion on World Wide Web
Measuring and addressing the impact of cold start on associative tag recommenders
Proceedings of the 19th Brazilian symposium on Multimedia and the web
Music Recommendation Based on Multidimensional Description and Similarity Measures
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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Music listeners frequently use words to describe music. Personalized music recommendation systems such as Last.fm and Pandora rely on manual annotations (tags) as a mechanism for querying and navigating large music collections. A well-known issue in such recommendation systems is known as the cold-start problem: it is not possible to recommend new songs/tracks until those songs/tracks have been manually annotated. Automatic tag annotation based on content analysis is a potential solution to this problem and has recently been gaining attention. We describe how stacked generalization can be used to improve the performance of a state-of-the-art automatic tag annotation system for music based on audio content analysis and report results on two publicly available datasets.