k-Hit Query: Top-k Query with Probabilistic Utility Function
Summary: Introduces k-hit queries: top-k selection under a probabilistic utility distribution to maximize the chance that a selected set contains a user’s favorite. Proposes k-hit_Alg, derives core properties, and shows empirical superiority over baselines on top-k under uncertainty tasks. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,378 | Exact Processing of Uncertain Top-k Queries in Multi-criteria Settings | 2018 | VLDB | 5.0844506e-05 |
| 8,581 | Geometric Approaches for Top-k Queries | 2017 | VLDB | 4.4874332e-05 |
| 8,812 | Creating Top Ranking Options in the Continuous Option and Preference Space | 2019 | VLDB | 4.4397776e-05 |
| 8,823 | Determining the Impact Regions of Competing Options in Preference Space | 2017 | SIGMOD | 4.4372904e-05 |
| 9,755 | Interactive Search for One of the Top-k | 2021 | SIGMOD | 4.2856385e-05 |
| 11,043 | Robust Best Point Selection under Unreliable User Feedback | 2024 | VLDB | 4.1905499e-05 |
| 11,197 | rkHit: Representative Query with Uncertain Preference | 2023 | SIGMOD | 4.1905499e-05 |
| 11,369 | tau-LevelIndex: Towards Efficient Query Processing in Continuous Preference Space | 2022 | SIGMOD | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 913 | Finding k-Dominant Skylines in High Dimensional Space | 2006 | SIGMOD | 0.00015372758 |
| 1,070 | Regret-Minimizing Representative Databases | 2010 | VLDB | 0.00014274615 |
| 1,435 | Diversifying Top-K Results | 2012 | VLDB | 0.00011981694 |
| 2,004 | Discovering Relative Importance of Skyline Attributes | 2009 | VLDB | 9.8183624e-05 |
| 2,474 | Top-k Bounded Diversification | 2012 | SIGMOD | 8.6956353e-05 |
| 3,190 | Top-k Queries on Uncertain Data: On Score Distribution and Typical Answers | 2009 | SIGMOD | 7.413338e-05 |
| 4,718 | Answering Top-k Queries with Multi-Dimensional Selections: The Ranking Cube Approach | 2006 | VLDB | 5.9664602e-05 |
| 5,988 | Call to Order: A Hierarchical Browsing Approach to Eliciting Users' Preference | 2010 | SIGMOD | 5.2394981e-05 |
| 7,434 | On Efficient Top-k Query Processing in Highly Distributed Environments | 2008 | SIGMOD | 4.7270401e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 12,016 | Generating Top-k Packages via Preference Elicitation | 2014 | VLDB | 4.1905499e-05 |
| 3,190 | Top-k Queries on Uncertain Data: On Score Distribution and Typical Answers | 2009 | SIGMOD | 7.413338e-05 |
| 7,966 | Efficient Top-K Processing Over Query-Dependent Functions | 2008 | VLDB | 4.6089395e-05 |
| 12,119 | Optimal Top-k Generation of Attribute Combinations based on Ranked Lists | 2012 | SIGMOD | 4.1905499e-05 |
| 10,376 | A Rank-Based Approach to Recommender System’s Top-K Queries with Uncertain Scores | 2025 | SIGMOD | 4.1905499e-05 |
| 9,755 | Interactive Search for One of the Top-k | 2021 | SIGMOD | 4.2856385e-05 |
| 1,805 | Top-k Query Evaluation with Probabilistic Guarantees | 2004 | VLDB | 0.00010479371 |
| 5,259 | Efficient k-Regret Query Algorithm with Restriction-free Bound for any Dimensionality | 2018 | SIGMOD | 5.5972902e-05 |
| 11,197 | rkHit: Representative Query with Uncertain Preference | 2023 | SIGMOD | 4.1905499e-05 |
| 6,378 | Exact Processing of Uncertain Top-k Queries in Multi-criteria Settings | 2018 | VLDB | 5.0844506e-05 |