Multi-Batches Revenue Maximization for competitive products over online social network

Enterprises can promote their products widely and rapidly over Online Social Network (OSN) through viral marketing. Viral marketing means an enterprise gives samples of one product to some individuals in OSN, such that the individuals promote the product to their fans and fans’ fans. The information...

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Vydáno v:Journal of network and computer applications Ročník 201; s. 103357
Hlavní autoři: Liang, Guangxian, Yao, Xiaopeng, Gu, Yue, Huang, Hejiao, Gu, Chonglin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.05.2022
Témata:
Approximate algorithm
Revenue maximization
Social networks
Revenue maximization
Social networks
Approximate algorithm
ISSN:1084-8045, 1095-8592
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Shrnutí:Enterprises can promote their products widely and rapidly over Online Social Network (OSN) through viral marketing. Viral marketing means an enterprise gives samples of one product to some individuals in OSN, such that the individuals promote the product to their fans and fans’ fans. The information about the product diffuses virally over OSN, and enterprise gains great revenue from the sales. An enterprise which produces different types of products, called competitive products, should conduct viral marketing more than one batch to maximize revenue. Since each individual normally purchase one from different types, the earlier batch product occupies more customers than the later batch product. The enterprise should find a seed sets sequence for Multi-Batches Revenue Maximization (MBRM). In this paper, we observe the behavior of individuals promoting and purchasing products is independent, and propose a Multi-Batches Independent Cascade model. We comprehensively study how to select the seed sets sequence with adaptive method. Since MBRM combines Influence Maximization and integer knapsack problem, MBRM is NP-hard and needs to be optimally analyzed with dynamic programming method. We propose a computation framework with an approximation algorithm using price-aware Reach Reverse-set(p-RRset) and a heuristic algorithm. The experiments on four real OSN datasets show the efficiency and effectiveness of our algorithms.
ISSN:1084-8045
1095-8592
DOI:10.1016/j.jnca.2022.103357