A framework for expressing and combining preferences
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Preference formulas in relational queries
ACM Transactions on Database Systems (TODS)
Personalization of Queries in Database Systems
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Foundations of preferences in database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Fast contextual preference scoring of database tuples
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
A data-oriented survey of context models
ACM SIGMOD Record
Contextual Ranking of Database Querying Results: A Statistical Approach
EuroSSC '08 Proceedings of the 3rd European Conference on Smart Sensing and Context
A methodology for preference-based personalization of contextual data
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Ranking Query Results using Context-Aware Preferences
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
And what can context do for data?
Communications of the ACM - Scratch Programming for All
Journal of Artificial Intelligence Research
Discovering relative importance of skyline attributes
Proceedings of the VLDB Endowment
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User preferences are a fundamental ingredient of personalized database applications, in particular those in which the user context plays a key role. Given a set of preferences defined in different contexts, in this paper we study the problem of deriving the preferences that hold in one of them, that is, how preferences propagate through contexts. For the sake of generality, we work with an abstract context model, which only requires that the contexts form a poset. We first formalize the basic properties of the propagation process: specificity, stating that more specific contexts prevail on less specific ones, and fairness, stating that this behavior does not hold for incomparable contexts. We then introduce an algebraic model for preference propagation that relies on two well-known operators for combining preferences: Pareto and Prioritized composition. We study three alternative propagation methods and precisely characterize them in terms of the fairness and specificity properties.