User Similarity

User similarity research focuses on creating effective representations of users to capture their preferences and behaviors for applications like recommendation systems and targeted advertising. Current research emphasizes multi-view learning approaches, incorporating diverse data sources (e.g., text, activity patterns, network interactions) and leveraging architectures like transformers and graph neural networks to generate robust user embeddings. These advancements enable improved user matching, personalized recommendations, and the detection of anomalies like fake accounts or coordinated disinformation campaigns, impacting various fields from e-commerce to social media analysis.