Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
The cost structure of sensemaking
INTERCHI '93 Proceedings of the INTERCHI '93 conference on Human factors in computing systems
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Clustering with Instance-Level Constraints
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning with matrix factorizations
Learning with matrix factorizations
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Clustering with Interactive Feedback
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Collaborative clustering with background knowledge
Data & Knowledge Engineering
A contextual-bandit approach to personalized news article recommendation
Proceedings of the 19th international conference on World wide web
Transfer metric learning by learning task relationships
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the fourth ACM international conference on Web search and data mining
Apolo: making sense of large network data by combining rich user interaction and machine learning
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Collaborative topic modeling for recommending scientific articles
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Foundations and Trends® in Machine Learning
Regroup: interactive machine learning for on-demand group creation in social networks
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Information cartography: creating zoomable, large-scale maps of information
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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We study the problem of learning personalized user models from rich user interactions. In particular, we focus on learning from clustering feedback (i.e., grouping recommended items into clusters), which enables users to express similarity or redundancy between different items. We propose and study a new machine learning problem for personalization, which we call collaborative clustering. Analogous to collaborative filtering, in collaborative clustering the goal is to leverage how existing users cluster or group items in order to predict similarity models for other users' clustering tasks. We propose a simple yet effective latent factor model to learn the variability of similarity functions across a user population. We empirically evaluate our approach using data collected from a clustering interface we developed for a goal-oriented data exploration (or sensemaking) task: asking users to explore and organize attractions in Paris. We evaluate using several realistic use cases, and show that our approach learns more effective user models than conventional clustering and metric learning approaches.