Trend prediction in temporal bipartite networks: the case of Movielens, Netflix, and Digg

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.

9 pages, 1 table, 5 figures

References in corpus (5)

• Temporal Networks
• Leaders in Social Networks, the Delicious Case
• Robust dynamic classes revealed by measuring the response function of a social system
• Ranking Scientific Publications Using a Simple Model of Network Traffic
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Cited by in corpus (7)

• Ranking in evolving complex networks
• Network-based recommendation algorithms: A review
• The essential role of time in network-based recommendation
• Temporal effects in trend prediction: identifying the most popular nodes in the future
• Rising Novelties on Evolving Networks: Recent Behavior Dominant and Non-Dominant Model
• Predicting Item Popularity: Analysing Local Clustering Behaviour of Users
• Individual-driven versus interaction-driven burstiness in human dynamics: The case of Wikipedia edit history