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
PREFER: a system for the efficient execution of multi-parametric ranked queries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Querying with Intrinsic Preferences
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Preferences; Putting More Knowledge into Queries
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
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
Foundations of preferences in database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Metadata inference for document retrieval in a distributed repository
ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday
Designing personalized curricula based on student preferences
SIGDOC '07 Proceedings of the 25th annual ACM international conference on Design of communication
Exploiting preference queries for searching learning resources
EC-TEL'07 Proceedings of the Second European conference on Technology Enhanced Learning: creating new learning experiences on a global scale
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We consider a collection of federated sources on the Web, and a community of users who are interested in documents residing in one or more of those federated sources. The search for documents of interest is supported by a mediator that we call a digital library. The library simply indexes all documents that are made available to users by the federated sources. When a user addresses a query to the library, the library returns the URLs of documents satisfying the query. In such a context, one of the factors influencing user satisfaction is the size of the answer set, in particular when it is too small (few or no documents) or too large (several hundreds or thousands of documents). In this paper, we address the problem of answer sets that are too large, and we call personalized query a usual query together with (a) an upper bound on the number of documents returned, and (b) a set of preferences as to the order in which the returned documents should be presented to the user; both these parameters are defined by the user online, during query formulation. The main contribution of the paper is to propose a framework in which the problem can be stated formally, and a method for the evaluation of personalized queries.