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)
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Authors
- 1. Min Xie
- 2. Laks V.S. Lakshmanan
- 3. Peter T. Wood
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 707 | Foundations of Preferences in Database Systems | 2002 | VLDB | 0.00017782998 |
| 1,998 | Discovering Relative Importance of Skyline Attributes | 2009 | VLDB | 9.824482e-05 |
| 2,979 | FlexRecs: Expressing and Combining Flexible Recommendations | 2009 | SIGMOD | 7.7844948e-05 |
| 3,014 | Ranking with Uncertain Scoring Functions: Semantics and Sensitivity Measures | 2011 | SIGMOD | 7.70946e-05 |
| 5,985 | Call to Order: A Hierarchical Browsing Approach to Eliciting Users' Preference | 2010 | SIGMOD | 5.2442779e-05 |
| 6,079 | Querying Uncertain Data with Aggregate Constraints | 2011 | SIGMOD | 5.2223439e-05 |
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