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Generating Top-k Packages via Preference Elicitation

Summary: Models preferences over packages with a learnable linear utility under uncertainty, updated via preference elicitation from user feedback. Offers sampling-based learning and an efficient top-k package generator with multiple ranking semantics, addressing hard constraints and Pareto overload. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10891
Venue
VLDB
Year
2014
Pagerank
4.1945683e-05
Overall Rank
12,008 | 16.47%
DOI
-

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