RecForUS: A Recommender System for Uncertain Scores
Summary: RecForUs: demo recommender handling uncertain item scores and user-specific ranking objectives via participant-vs-algorithm competition. RankDist efficiently computes rank probabilities to produce top-K under multiple ranking semantics without enumerating possible worlds. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Dvir Cohen
- 2. Liad Domb
- 3. Avigdor Gal
- 4. Lior Ganon
- 5. Eliezer Gavriel
- 6. Omri Lazover
- 7. Coral Scharf
- 8. Bar Shterenberg
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,364 | A Rank-Based Approach to Recommender System’s Top-K Queries with Uncertain Scores | 2025 | SIGMOD | 4.1945683e-05 |
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