# The topological face of recommendation: models and application to bias detection

## TL;DR

This paper introduces a graph-based model for analyzing recommendation systems, focusing on detecting biases through topological analysis of recommendation graphs, with applications to YouTube's recommendation links.

## Contribution

It proposes a novel topological approach to detect recommendation bias by analyzing the structure of recommendation graphs, enhancing transparency in algorithmic recommendations.

## Key findings

- Topological features can indicate recommendation bias.
- The approach successfully predicts biased recommendations on YouTube.
- Graph analysis reveals coherence in recommendation networks.

## Abstract

Recommendation plays a key role in e-commerce and in the entertainment industry. We propose to consider successive recommendations to users under the form of graphs of recommendations. We give models for this representation. Motivated by the growing interest for algorithmic transparency, we then propose a first application for those graphs, that is the potential detection of introduced recommendation bias by the service provider. This application relies on the analysis of the topology of the extracted graph for a given user; we propose a notion of recommendation coherence with regards to the topological proximity of recommended items (under the measure of items' k-closest neighbors, reminding the "small-world" model by Watts & Stroggatz). We finally illustrate this approach on a model and on Youtube crawls, targeting the prediction of "Recommended for you" links (i.e., biased or not by Youtube).