Machine Learning Algorithms for Personalized User Engagement in Social Media

• Author(s): Sreeprasad Govindankutty ; Kratika Jain
• Paper ID: 1706713
• Page: 874-897
• Published Date: 21-12-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 5 November-2024

Abstract

Social media platforms have become integral to modern communication and information exchange, necessitating the development of strategies to enhance user engagement. Personalized user experiences are critical in retaining users and fostering meaningful interactions, and machine learning (ML) algorithms offer innovative solutions to achieve this. This paper investigates the role of machine learning in personalizing user engagement by analyzing user data and behavioral patterns. Techniques such as collaborative filtering, content-based filtering, and hybrid models are employed to recommend relevant content, connect users with similar interests, and optimize platform interactions. Additionally, natural language processing (NLP) is utilized to assess user sentiment, preferences, and interests, enabling platforms to deliver tailored content in real time. Advanced algorithms, including deep learning and reinforcement learning, are explored for their ability to dynamically adapt to user behavior and improve recommendation accuracy. The study also addresses key challenges such as algorithmic bias, data privacy, and ethical considerations, emphasizing the need for transparent and secure data handling practices. By leveraging these algorithms, social media platforms can foster a more personalized and engaging environment, enhancing user satisfaction and loyalty. The research concludes with insights into emerging trends in ML for social media personalization, highlighting the potential of integrating AI-driven models to predict and influence user engagement patterns effectively. The findings underscore the transformative role of machine learning in revolutionizing how users interact with social media platforms, paving the way for more immersive and meaningful digital experiences.

Keywords

Machine learning, personalized user engagement, social media, collaborative filtering, content-based filtering, hybrid models, natural language processing, sentiment analysis, deep learning, reinforcement learning, data privacy, algorithmic bias, user behavior, recommendation systems, digital experiences.

Citations

IRE Journals:
Sreeprasad Govindankutty , Kratika Jain "Machine Learning Algorithms for Personalized User Engagement in Social Media" Iconic Research And Engineering Journals Volume 8 Issue 5 2024 Page 874-897

IEEE:
Sreeprasad Govindankutty , Kratika Jain "Machine Learning Algorithms for Personalized User Engagement in Social Media" Iconic Research And Engineering Journals, 8(5)