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ProfileRank: Finding Relevant Content and Influential Users based on Information Diffusion ∗
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BibTeX
@MISC{Silva_profilerank:finding,
author = {Arlei Silva and Sara Guimarães and Wagner Meira and Mohammed Zaki},
title = {ProfileRank: Finding Relevant Content and Influential Users based on Information Diffusion ∗},
year = {}
}
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Abstract
Understanding information diffusion processes that take place on the Web, specially in social media, is a fundamental step towards the design of effective information diffusion mechanisms, recommendation systems, and viral marketing/advertising campaigns. Two key concepts in information diffusion are influence and relevance. Influence is the ability to popularize content in an online community. To this end, influentials introduce and propagate relevant content, in the sense that such content satisfies the information needs of a significant portion of this community. In this paper, we study the problem of identifying influential users and relevant content in information diffusion data. We propose ProfileRank, a new information diffusion model based on random walks over a user-content graph. ProfileRank is a PageRank inspired model that exploits the principle that relevant content is created and propagated by influential users and influential users create relevant content. A convenient property of ProfileRank is that it can be adapted to provide personalized recommendations. Experimental results demonstrate that ProfileRank makes accurate recommendations, outperforming baseline techniques. We also illustrate relevant content and influential users discovered using ProfileRank. Our analysis shows that ProfileRank scores are more correlated with content diffusion than with the network structure. We also show that our new modeling is more efficient than PageRank to perform these calculations.
Keyphrases
influential user relevant content information diffusion finding relevant content pagerank inspired model effective information diffusion mechanism social medium convenient property new modeling new information diffusion model information diffusion data personalized recommendation content diffusion profilerank score online community baseline technique random walk significant portion accurate recommendation key concept recommendation system information need user-content graph viral marketing advertising campaign network structure experimental result fundamental step
