Using PageRank for non-personalized default rankings in dynamic markets

DOI10.1016/j.ejor.2016.12.022zbMath1402.90069OpenAlexW2568969585WikidataQ58205374 ScholiaQ58205374MaRDI QIDQ1753450

Franz Rothlauf, Jella Pfeiffer, Michael Scholz

Publication date: 29 May 2018

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2016.12.022

zbMATH Keywords

laboratory experimentPageRankE-commerceproduct domination graphproduct ranking algorithm

Mathematics Subject Classification ID

Decision theory (91B06) Small world graphs, complex networks (graph-theoretic aspects) (05C82) Management decision making, including multiple objectives (90B50) Financial applications of other theories (91G80)

Related Items (5)

Mining twitter lists to extract brand-related associative information for celebrity endorsement ⋮ Non-backtracking PageRank: from the classic model to Hashimoto matrices ⋮ Tensorial graph learning for link prediction in generalized heterogeneous networks ⋮ On the spectrum of two-layer approach and multiplex PageRank ⋮ Parametric controllability of the personalized PageRank: Classic model vs biplex approach

Cites Work

• Unnamed Item
• The SMAA-PROMETHEE method
• A multiple criteria sorting method where each category is characterized by several reference actions: the Electre Tri-nC method
• Willingness-to-pay estimation with choice-based conjoint analysis: addressing extreme response behavior with individually adapted designs
• How to make a decision: The analytic hierarchy process
• How to select and how to rank projects: The PROMETHEE method
• Analytic hierarchy process: an overview of applications
• PageRank Beyond the Web
• The Perron–Frobenius Theorem and the Ranking of Football Teams

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