A Rank-Based Approach to Recommender System’s Top-K Queries with Uncertain Scores
Summary: Rank-based top-K queries for recommender systems with uncertain scores. Introduces RankDist to compute the probability of each item's rank under score distributions, enabling probabilistic ranking with guaranteed expected-optimality and empirical superiority over score-based baselines on standard benchmarks. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Coral Scharf (Technion)
- 2. Carmel Domshlak (Technion)
- 3. Avigdor Gal (Technion)
- 4. Haggai Roitman (Ben Gurion University)
BibTeX Citation
@inproceedings{scharf_sigmod25,
title = {{A Rank-Based Approach to Recommender System’s Top-K Queries with Uncertain Scores}},
author = {Scharf, Coral and Domshlak, Carmel and Gal, Avigdor and Roitman, Haggai},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709655},
url = {https://dl.acm.org/doi/10.1145/3709655},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 13,652 | RecForUS: A Recommender System for Uncertain Scores | 2025 | VLDB | - |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 84 | ULDBs: Databases with Uncertainty and Lineage | 2006 | VLDB | 0.00035963861 |
| 1,293 | Finding Related Tables in Data Lakes for Interactive Data Science | 2020 | SIGMOD | 0.00011149857 |
| 1,365 | A Unified Approach to Ranking in Probabilistic Databases | 2009 | VLDB | 0.00010913898 |
| 1,396 | Ranking Queries on Uncertain Data: A Probabilistic Threshold Approach | 2008 | SIGMOD | 0.0001079253 |
| 3,105 | Top-k Queries on Uncertain Data: On Score Distribution and Typical Answers | 2009 | SIGMOD | 7.6445188e-05 |
| 3,542 | Consensus Answers for Queries over Probabilistic Databases | 2009 | PODS | 7.2155312e-05 |
| 6,738 | Efficiently Answering Durability Prediction Queries | 2021 | SIGMOD | 5.6918011e-05 |
| 9,373 | Query-Guided Resolution in Uncertain Databases | 2023 | SIGMOD | 5.1868213e-05 |
| 9,374 | Efficient Approximation of Certain and Possible Answers for Ranking and Window Queries over Uncertain Data | 2023 | VLDB | 5.1868213e-05 |
| 9,380 | JENNER: Just-in-time Enrichment in Query Processing | 2022 | VLDB | 5.1868213e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 1,886 | Probabilistic Ranking of Database Query Results | 2004 | VLDB |
| 2 | 13,652 | RecForUS: A Recommender System for Uncertain Scores | 2025 | VLDB |
| 3 | 9,607 | Ranking Distributed Probabilistic Data | 2009 | SIGMOD |
| 4 | 8,514 | URank: Formulation and Efficient Evaluation of Top-k Queries in Uncertain Databases | 2007 | SIGMOD |
| 5 | 3,893 | Ranking Continuous Probabilistic Datasets | 2010 | VLDB |
| 6 | 1,365 | A Unified Approach to Ranking in Probabilistic Databases | 2009 | VLDB |
| 7 | 1,396 | Ranking Queries on Uncertain Data: A Probabilistic Threshold Approach | 2008 | SIGMOD |
| 8 | 3,105 | Top-k Queries on Uncertain Data: On Score Distribution and Typical Answers | 2009 | SIGMOD |
| 9 | 2,620 | Ranking with Uncertain Scoring Functions: Semantics and Sensitivity Measures | 2011 | SIGMOD |
| 10 | 6,176 | Exact Processing of Uncertain Top-k Queries in Multi-criteria Settings | 2018 | VLDB |