The Topological Face of Recommendation



The Topological Face of Recommendation

The Topological Face of Recommendation
Research Paper / Complex Networks / Nov 2017 / Machine/Deep Learning/AI, Network & Communications

The success of Google’s PageRank algorithm popularized graphs as a tool to model the web’s navigability. At that time, the web topology was resulting from human edition of hyper-links. Nowadays, that topology is mostly resulting from algorithms. In this paper, we propose to study the topology realized by a class of such algorithms: recommenders. By modeling the output of recommenders as graphs, we show that a vast array of topological observations become easily accessible, using a simple webcrawler. We give models and illustrations for those graph representations.

We then propose a graph-based methodology for addressing an algorithmic transparency problem: recommendation bias detection. We illustrate this approach on YouTube crawls, targeting the prediction of ”Recommended for you” links.