Predicting content consumption from content-to-content relationships

Hyun-chul Kim

Abstract:

As the majority of Internet traffic today is attributed to content-centric applications, there has been everincreasing demand for highly scalable and efficient content delivery. An accurate prediction on future content
consumption is essential for such demand. To address such an issue, this paper introduces a new computational
approach, Content Network (CN) that can capture the relations among contents, and its potential applications.
We conduct a measurement study to investigate how contents are inter-related from the viewpoint of content
spreading on one of the popular BitTorrent portals: The Pirate Bay. Based on the large-scale dataset that contains
18 K torrents and 9 M users, we construct the CN and investigate its structural properties. Our key finding is
that contents in the same community in the CN (i) belong to the same content category with 94% probability,
(ii) are uploaded by the same content publisher with 76% probability, and (iii) have the similar titles with 51%
probability, which implies that contents in the same community collectively contain common (shared) interests
of users. Our trace-driven study demonstrates that the proposed CN model is useful in (i) content recommendation for increasing sales and (ii) content caching for networking efficiency. We believe our work can provide
an important insight for content stakeholders, e.g., content providers for efficient publishing strategies, network
engineers for networking efficiency, or content marketers for accurate recommendation.