Exploiting social capital for improving personalized recommendations in online social networks

Abstract

This article presents a novel approach to enhancing personalized recommendations in online social networks by exploiting the concept of Social Capital. Recognizing the challenges posed by information overload on platforms like Twitter, the proposed method integrates user interactions and features on social media with the concept of Social Capital to generate more relevant recommendations. The model incorporates a multifaceted analysis of user data, including the user’s reputation, influence, and engagement strength, alongside tweets’ recency, diversity, and context scores, to calculate a comprehensive Social Capital Score for each recommendation. An extensive offline evaluation of the model demonstrates its effectiveness in improving the personalization and relevance of recommendations. The findings highlight the significant role of incorporating Social Capital in recommender systems, contributing to a more engaging and informative user experience on social media platforms. This study provides valuable insights into the potential benefits and limitations of this approach, advancing the field of recommender systems and enhancing our understanding of the role of social capital in shaping user behavior and preferences.