RoK: Roll-Up with the K-Means Clustering Method for Recommending OLAP Queries
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
The complexity of learning separable ceteris paribus preferences
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Layered evaluation of interactive adaptive systems: framework and formative methods
User Modeling and User-Adapted Interaction
Preference elicitation in prioritized skyline queries
The VLDB Journal — The International Journal on Very Large Data Bases
User-centric principles in automated decision making
SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence
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We consider the problem of modeling and reasoning about statements of ordinal preferences expressed by a user, such as monadic statement like "X is good," dyadic statements like "X is better than Y," etc. Such qualitative statements may be explicitly expressed by the user, or may be inferred from observable user behavior. This paper presents a novel technique for efficient reasoning about sets of such preference statements in a semantically rigorous manner. Specifically, we propose a novel approach for generating an ordinal utility function from a set of qualitative preference statements, drawing upon techniques from knowledge representation and machine learning. We provide theoretical evidence that the new method provides an efficient and expressive tool for reasoning about ordinal user preferences. Empirical results further confirm that the new method is effective on real-world data, making it promising for a wide spectrum of applications that require modeling and reasoning about user preferences.