User profiles matching for different social networks based on faces embeddings
This addresses the need for integrated user data across social media for applications like recommender systems and risk assessments, but it is incremental as it builds on existing face embedding techniques.
The paper tackles the problem of matching user profiles across different social networks by using embeddings from publicly available face photos, achieving stability to content and style variations in an experimental study.
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.