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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)

Paper ID
h3b44d80e2431eec3
Venue
SIGMOD
Year
2021
Pagerank
5.0806786e-05
Overall Rank
10,091 | 32.18%
DOI
10.1145/3448016.3452832

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{tang_sigmod21,
        title = {{On m-Impact Regions and Standing Top-k Influence Problems}},
        author = {Tang, Bo and Mouratidis, Kyriakos and Han, Mingji},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3452832},
        url = {https://dl.acm.org/doi/10.1145/3448016.3452832},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
11,718 rkHit: Representative Query with Uncertain Preference 2023 SIGMOD 4.9769913e-05
11,880 tau-LevelIndex: Towards Efficient Query Processing in Continuous Preference Space 2022 SIGMOD 4.9769913e-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.

Rank Cited Paper Year Venue Pagerank
5 Optimal Aggregation Algorithms for Middleware [Extended Abstract] 2001 PODS 0.0010679903
342 The Onion Technique: Indexing for Linear Optimization Queries 2000 SIGMOD 0.00020520649
418 PREFER: A System for the Efficient Execution of Multiparametric Ranked Queries 2001 SIGMOD 0.00018609134
545 Influence Sets Based on Reverse Nearest Neighbor Queries 2000 SIGMOD 0.00016599925
1,087 Regret-Minimizing Representative Databases 2010 VLDB 0.00012092041
2,485 Processing a Large Number of Continuous Preference Top-k Queries 2012 SIGMOD 8.3954142e-05
2,786 DADA: A Data Cube for Dominant Relationship Analysis 2006 SIGMOD 8.0127919e-05
2,958 Answering Why-not Questions on Reverse Top-k Queries 2015 VLDB 7.80653e-05
4,083 Learning User Preferences By Adaptive Pairwise Comparison 2015 VLDB 6.8143727e-05
4,242 Creating Competitive Products 2009 VLDB 6.705389e-05
4,548 Identifying the Most Influential Data Objects with Reverse Top-k Queries 2010 VLDB 6.5394775e-05
4,616 Branch-and-Bound Algorithm for Reverse Top-k Queries 2013 SIGMOD 6.4983802e-05
4,833 Efficient Computation of Regret-ratio Minimizing Set: A Compact Maxima Representative 2017 SIGMOD 6.3867086e-05
5,071 Efficient k-Regret Query Algorithm with Restriction-free Bound for any Dimensionality 2018 SIGMOD 6.2863925e-05
5,627 Reconciling Skyline and Ranking Queries 2017 VLDB 6.0578274e-05
5,916 Reverse k-Ranks Query 2014 VLDB 5.9472799e-05
6,178 Exact Processing of Uncertain Top-k Queries in Multi-criteria Settings 2018 VLDB 5.8581814e-05
6,382 Maximum Rank Query 2015 VLDB 5.805942e-05
9,002 Creating Top Ranking Options in the Continuous Option and Preference Space 2019 VLDB 5.23944e-05
9,181 Determining the Impact Regions of Competing Options in Preference Space 2017 SIGMOD 5.2115802e-05
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