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Abstract: Push-based data delivery requires knowledge of user interests for making scheduling, bandwidth allocation, and routing decisions. Such information is maintained as user profiles. We propose a new incremental algorithm for constructing user profiles based on monitoring and user feedback. In contrast to earlier approaches, which typically represent profiles as a single weighted interest vector, we represent user profiles as multiple interest vectors, whose number, size, and elements change... (Update)
Context of citations to this paper: More
.... of XML documents (i.e. the XFilter system) AF00] 2) learning and maintaining user profiles based on explicit user feedback [CFG00] and 3) development of a large scale, reliable system for mobile device synchroniz ation [DF00] Data recharging can build upon this work,...
.... it unlikely that most users will write profiles manually (the same could be said for SQL) Instead, we expect that a profile generation system [7] with good interfaces could support users to this end. Such a system could rely on libraries of parameterized profiles that are...
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BibTeX entry: (Update)
U. Cetintemel, M. Franklin, C. L. Giles, "SelfAdaptive User Profiles for Large Scale Data Delivery ", Proc. 16th ICDE, San Diego, February, 2000. http://citeseer.ist.psu.edu/cetintemel00selfadaptive.html More
@inproceedings{ cetintemel00selfadaptive,
author = "Ugur Cetintemel and Michael J. Franklin and C. Lee Giles",
title = "Self-Adaptive User Profiles for Large-Scale Data Delivery",
booktitle = "{ICDE}",
pages = "622-633",
year = "2000",
url = "citeseer.ist.psu.edu/cetintemel00selfadaptive.html" }
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