On m-Impact Regions and Standing Top-k Influence Problems
Summary: Defines m-Impact Regions (mIR) to identify attribute-value regions that place a product in the top-k for a user fraction, enabling targeted marketing and product refinement. Delivers exact, scalable algorithms for standing top-k influence, outperforming heuristics with real-review data experiments. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Bo Tang
- 2. Kyriakos Mouratidis
- 3. Mingji Han
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 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 20 of 20 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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