The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Solving concisely expressed combinatorial auction problems
Eighteenth national conference on Artificial intelligence
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Combinatorial Auctions
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Preference learning with Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Optimal recommendation sets: covering uncertainty over user preferences
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
DD-PREF: a language for expressing preferences over sets
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Predicting diverse subsets using structural SVMs
Proceedings of the 25th international conference on Machine learning
Mixing it up: recommending collections of items
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Enhancing diversity, coverage and balance for summarization through structure learning
Proceedings of the 18th international conference on World wide web
Learning optimal subsets with implicit user preferences
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
IEEE Transactions on Information Theory
Creating collections with automatic suggestions and example-based refinement
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Robot self-initiative and personalization by learning through repeated interactions
Proceedings of the 6th international conference on Human-robot interaction
Clustering rankings in the fourier domain
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
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Most work on preference learning has focused on pairwise preferences or rankings over individual items. In this paper, we present a method for learning preferences over sets of items. Our learning method takes as input a collection of positive examples---that is, one or more sets that have been identified by a user as desirable. Kernel density estimation is used to estimate the value function for individual items, and the desired set diversity is estimated from the average set diversity observed in the collection. Since this is a new learning problem, we introduce a new evaluation methodology and evaluate the learning method on two data collections: synthetic blocks-world data and a new real-world music data collection that we have gathered.