Mining Revenue-Maximizing Bundling Configuration

Summary: Formulates revenue-maximizing bundle configuration as mining consumer preferences from rating data, proving NP-hardness for bundles larger than two items. Provides an optimal algorithm for pairs and scalable heuristics for larger bundles, validated on real-world data. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 11358
Venue: VLDB
Year: 2015
Pagerank: 5.214913e-05
Overall Rank: 9,822 | 32.62%
DOI: 10.14778/2735508.2735517

Incoming Non-self Citations Over Time

Authors

1. Loc Do (Singapore Management University)
2. Hady W. Lauw (Singapore Management University)
3. Ke Wang (Simon Fraser University)

BibTeX Citation

@article{do_vldb15,
        title = {{Mining Revenue-Maximizing Bundling Configuration}},
        author = {Do, Loc and Lauw, Hady W. and Wang, Ke},
        journal = {PVLDB},
        series = {{VLDB} '15},
        volume = {8},
        number = {5},
        pages = {593--604},
        doi = {10.14778/2735508.2735517},
        url = {https://doi.org/10.14778/2735508.2735517},
        year = {2015}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,919	X-Blossom: Massive Parallelization of Graph Maximum Matching	2025	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
27	Fast Algorithms for Mining Association Rules	1994	VLDB	0.00052255472
4,758	Constructing and Exploring Composite Items	2010	SIGMOD	6.521122e-05

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