# User profiles matching for different social networks based on faces embeddings

## TL;DR

This paper introduces a face-embedding based method for matching user profiles across different social networks, enabling better understanding of human behavior for applications like recommendations and sociological research.

## Contribution

It presents a novel face-embedding approach for cross-platform user profile matching and demonstrates its stability across various social media content styles.

## Key findings

- Effective user profile matching across social networks
- Stable performance despite content and style variations
- Potential applications in recommendations and sociology

## Abstract

It is common practice nowadays to use multiple social networks for different social roles. Although this, these networks assume differences in content type, communications and style of speech. If we intend to understand human behaviour as a key-feature for recommender systems, banking risk assessments or sociological researches, this is better to achieve using a combination of the data from different social media. In this paper, we propose a new approach for user profiles matching across social media based on embeddings of publicly available users' face photos and conduct an experimental study of its efficiency. Our approach is stable to changes in content and style for certain social media.