Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
User Modelling for Interactive User-Adaptive Collection Structuring
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
Learning Similarity Functions from Qualitative Feedback
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Input-agreement: a new mechanism for collecting data using human computation games
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Towards user-adaptive structuring and organization of music collections
AMR'08 Proceedings of the 6th international conference on Adaptive Multimedia Retrieval: identifying, Summarizing, and Recommending Image and Music
Similarity adaptation in an exploratory retrieval scenario
AMR'10 Proceedings of the 8th international conference on Adaptive Multimedia Retrieval: context, exploration, and fusion
Combining sources of description for approximating music similarity ratings
AMR'11 Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
Adapting similarity on the MagnaTagATune database: effects of model and feature choices
Proceedings of the 21st international conference companion on World Wide Web
Combining sources of description for approximating music similarity ratings
AMR'11 Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
Adaptive music retrieval---a state of the art
Multimedia Tools and Applications
Learning music similarity from relative user ratings
Information Retrieval
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Similarity plays an important role in many multimedia retrieval applications. However, it often has many facets and its perception is highly subjective --- very much depending on a person's background or retrieval goal. In previous work, we have developed various approaches for modeling and learning individual distance measures as a weighted linear combination of multiple facets in different application scenarios. Based on a generalized view of these approaches as an optimization problem guided by generic relative distance constraints, we describe ways to address the problem of constraint violations and finally compare the different approaches against each other. To this end, a comprehensive experiment using the Magnatagatune benchmark dataset is conducted.