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

TL;DR

This paper investigates predicting future popular items in online systems modeled as temporal bipartite networks, demonstrating improved accuracy when incorporating user social network centrality across datasets from Movielens, Netflix, and Digg.

Contribution

It introduces a method that leverages user social network centrality to enhance prediction accuracy in temporal bipartite networks.

Findings

Prediction performance improves with social network information.

Centrality-weighted actions outperform baseline methods.

Effective across multiple online service datasets.

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.