On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
A framework for expressing and combining preferences
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Preference queries in deductive databases
New Generation Computing
Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
Querying with Intrinsic Preferences
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Proceedings of the 17th International Conference on Data Engineering
Preferences; Putting More Knowledge into Queries
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A Formal Model for User Preference
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Statistical Model for User Preference
IEEE Transactions on Knowledge and Data Engineering
Database querying under changing preferences
Annals of Mathematics and Artificial Intelligence
Foundations of preferences in database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Preference SQL: design, implementation, experiences
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Mining Context-based User Preferences for m-Services Applications
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Journal of Artificial Intelligence Research
Discovering relative importance of skyline attributes
Proceedings of the VLDB Endowment
Proceedings of the VLDB Endowment
Personalizing queries based on networks of composite preferences
ACM Transactions on Database Systems (TODS)
Call to order: a hierarchical browsing approach to eliciting users' preference
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Efficient skyline evaluation over partially ordered domains
Proceedings of the VLDB Endowment
Preference elicitation in prioritized skyline queries
The VLDB Journal — The International Journal on Very Large Data Bases
BMC: an efficient method to evaluate probabilistic reachability queries
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Probabilistic skylines on uncertain data: model and bounding-pruning-refining methods
Journal of Intelligent Information Systems
Interactive regret minimization
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Mining contextual preference rules for building user profiles
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Skyline queries, front and back
ACM SIGMOD Record
Monochromatic and bichromatic mutual skyline queries
Expert Systems with Applications: An International Journal
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Mining user preferences plays a critical role in many important applications such as customer relationship management (CRM), product and service recommendation, and marketing campaigns. In this paper, we identify an interesting and practical problem of mining user preferences: in a multidimensional space where the user preferences on some categorical attributes are unknown, from some superior and inferior examples provided by a user, can we learn about the user's preferences on those categorical attributes? We model the problem systematically and show that mining user preferences from superior and inferior examples is challenging. Although the problem has great potential in practice, to the best of our knowledge, it has not been explored systematically before. As the first attempt to tackle the problem, we propose a greedy method and show that our method is practical using real data sets and synthetic data sets.