Identifying Top K persuaders using singular value decomposition

Yun Hong Min, Yerim Chung

Research output: Contribution to journalArticle

Abstract

Purpose - Finding top K persuaders in consumer network is an important problem in marketing. Recently, a new method of computing persuasion scores, interpreted as fixed point or stable distribution for given persuasion probabilities, was proposed. Top K persuaders are chosen according to the computed scores. This research proposed a new definition of persuasion scores relaxing some conditions on the matrix of probabilities, and a method to identify top K persuaders based on the defined scores. Research design, data, and methodology - A new method of computing top K persuaders is computed by singular value decomposition (SVD) of the matrix which represents persuasion probabilities between entities. Results - By testing a randomly generated instance, it turns out that the proposed method is essentially different from the previous study sharing a similar idea. Conclusions - The proposed method is shown to be valid with respect to both theoretical analysis and empirical test. However, this method is limited to the category of persuasion scores relying on the matrix-form of persuasion probabilities. In addition, the strength of the method should be evaluated via additional experiments, e.g., using real instances, different benchmark methods, efficient numerical methods for SVD, and other decomposition methods such as NMF.

Original languageEnglish
Pages (from-to)25-29
Number of pages5
JournalJournal of Distribution Science
Volume14
Issue number9
DOIs
Publication statusPublished - 2016 Sep 1

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All Science Journal Classification (ASJC) codes

  • Business and International Management
  • Economics and Econometrics
  • Marketing

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