TREND PREDICTION IN TEMPORAL BIPARTITE NETWORKS: THE CASE OF MOVIELENS, NETFLIX, AND DIGG

An Zeng, Stanislao Gualdi, Matúš Medo and Yi-Cheng Zhang ()
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An Zeng: Physics Department, University of Fribourg, CH-1700 Fribourg, Switzerland
Stanislao Gualdi: Physics Department, University of Fribourg, CH-1700 Fribourg, Switzerland
Matúš Medo: Physics Department, University of Fribourg, CH-1700 Fribourg, Switzerland
Yi-Cheng Zhang: Physics Department, University of Fribourg, CH-1700 Fribourg, Switzerland

Advances in Complex Systems (ACS), 2013, vol. 16, issue 04n05, 1-15

Abstract: Online systems, where users purchase or collect items of some kind, can be effectively represented by temporal bipartite networks where both nodes and links are added with time. We use this representation to predict which items might become popular in the near future. Various prediction methods are evaluated on three distinct datasets originating from popular online services (Movielens, Netflix, and Digg). We show that the prediction performance can be further enhanced if the user social network is known and centrality of individual users in this network is used to weight their actions.

Keywords: Prediction; popularity; social networks; e-commerce (search for similar items in EconPapers)
Date: 2013
References: View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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DOI: 10.1142/S0219525913500240

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