Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
9-2015
Abstract
With greater prevalence of social media, there is an increasing amount of user-generated data revealing consumer preferences for various products and services. Businesses seek to harness this wealth of data to improve their marketing strategies. Bundling, or selling two or more items for one price is a highly-practiced marketing strategy. In this paper, we address the bundle configuration problem from the data-driven perspective. Given a set of items in a seller’s inventory, we seek to determine which items should belong to which bundle so as to maximize the total revenue, by mining consumer preferences data. We show that this problem is NP-hard when bundles are allowed to contain more than two items. Therefore, we describe an optimal solution for bundle sizes up to two items, and propose two heuristic solutions for bundles of any larger size. We investigate the effectiveness and the efficiency of the proposed algorithms through experimentations on real-life rating-based preferences data.
Discipline
Computer Sciences | Databases and Information Systems
Publication
Proceedings of the VLDB Endowment: August 31-September 4, 2015, Kohala Coast, Hawaii
Volume
8
First Page
593
Last Page
604
Publisher
VLDB Endowment
City or Country
Saratoga, CA
Citation
DO, Loc; LAUW, Hady Wirawan; and WANG, Ke.
Mining Revenue-Maximizing Bundling Configuration. (2015). Proceedings of the VLDB Endowment: August 31-September 4, 2015, Kohala Coast, Hawaii. 8, 593-604.
Available at: https://ink.library.smu.edu.sg/sis_research/2630
Copyright Owner and License
LARC
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://www.vldb.org/pvldb/vol8/p593-do.pdf