Enhancing User Engagement and Content Relevance: A Novel Approach to Social Media Post Recommendation System - Dnyanesh Hajare; Shilpa Jahagirdar; Abhishek Humbe (International Journal of Algorithms Design and Analysis Review, 2024)
The social media post recommendation system is an innovative solution aimed at optimizing content delivery for users in today's digital age. Its primary motive is to tailor online experiences, ensuring users receive posts most relevant to their preferences. Various machine learning algorithms are employed to suggest related posts. This system leverages advanced algorithms and analytics, producing key results that highlight user engagement metrics and content relevance. Preliminary findings of this study suggest that a bespoke content delivery method significantly enhances user engagement. This system has the potential to redefine social media content recommendations. The primary aim of the system is to enhance user experience by tailoring online experiences to individual preferences. This entails providing users with highly relevant content to boost engagement and satisfaction. The system employs multiple machine learning algorithms to suggest related posts. These algorithms analyze user behavior, preferences, and interactions to generate personalized recommendations. By leveraging advanced algorithms and analytics, the system aims to produce key insights into user engagement metrics and content relevance. Overall, the social media post recommendation system represents a comprehensive and forward-thinking approach to optimizing content delivery in the digital age. By leveraging advanced technology and analytics, the system aims to provide users with a more personalized and engaging online experience.
Keywords: Information filtering; Social media content; personalization techniques; recommendation algorithms; user engagement