Data-driven Co-clustering Model of Internet Usage in Large Mobile Societies

TL;DR

This paper presents a data-driven co-clustering approach to model and analyze mobile Internet usage behaviors across large populations and multiple locations, revealing distinct user profiles and behavior patterns.

Contribution

It introduces a novel large-scale multi-dimensional co-clustering method for mobile Internet data and provides the first detailed analysis of user behavior across numerous locations.

Findings

Users can be modeled with ten distinct clusters.

Access patterns vary significantly across locations.

First detailed large-scale mobile Internet usage analysis.

Abstract

Design and simulation of future mobile networks will center around human interests and behavior. We propose a design paradigm for mobile networks driven by realistic models of users' on-line behavior, based on mining of billions of wireless-LAN records. We introduce a systematic method for large-scale multi-dimensional coclustering of web activity for thousands of mobile users at 79 locations. We find surprisingly that users can be consistently modeled using ten clusters with disjoint profiles. Access patterns from multiple locations show differential user behavior. This is the first study to obtain such detailed results for mobile Internet usage.