Abstract
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.
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These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This work is partially supported by the EU Network of Excellence in Digital Libraries (Delos NoE-6038-507618).
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Spyratos, N., Christophides, V. (2006). Querying with Preferences in a Digital Library. In: Jantke, K.P., Lunzer, A., Spyratos, N., Tanaka, Y. (eds) Federation over the Web. Lecture Notes in Computer Science(), vol 3847. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11605126_8
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DOI: https://doi.org/10.1007/11605126_8
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