GRAPH-BASED FRIEND RECOMMENDATIONS ON SOCIAL MEDIA USING MACHINE LEARNING

Social media platforms facilitate the exchange of vast amounts of information within their networks, where users and their friends form vast communities. To foster meaningful connections, recommendation systems play a vital role in linking users with similar interests. Existing social media networks rely on users' networks to suggest friends, but this approach may not accurately capture users' real-life preferences. This study presents a novel Facebook friend recommendation system utilizing the XGBoost algorithm, which analyzes users' social graphs, incorporates diverse connection parameters, and evaluates various similarity measures. Specifically, we employ similarity measures such as Jaccard distance and the Otsuka-Ochiai coefficient to filter graph information and calculate user ratings. The model leverages features extracted from Adar Index, Hits Score, Katz Centrality, and Page Rank. Our proposed system, utilizing both Random Forest and XGBoost algorithms, achieves 99.99% accuracy. Notably, XGBoost excels in efficiency and speed, making it an ideal choice for large-scale social networks. By suggesting friends with similar ratings, our system enhances user experience and fosters meaningful connections within the Facebook community.