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Machine Learning in User Engagement: Engineering Solutions for Social Media Platforms
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
In recent years, the integration of machine learning (ML) techniques has significantly transformed user engagement strategies on social media platforms. These platforms rely heavily on personalized experiences to drive user interaction, retention, and overall platform growth. This paper explores the role of machine learning in enhancing user engagement, focusing on the engineering solutions that underpin recommendation systems, sentiment analysis, and behavior prediction models. By utilizing data-driven insights, ML algorithms enable platforms to tailor content, advertisements, and social interactions to individual users, thus fostering a deeper connection with the platform. Moreover, the paper delves into the challenges faced in implementing ML solutions, including data privacy concerns, algorithmic biases, and scalability issues. The study also presents case examples from leading social media companies, illustrating the practical applications of ML in improving user experience and platform dynamics. Finally, future directions in the field, such as the integration of advanced deep learning models and real-time data processing, are discussed to highlight emerging trends in the engineering of user engagement strategies for social media platforms.
Keywords
Machine learning, user engagement, social media platforms, recommendation systems, sentiment analysis, behavior prediction, personalized content, data privacy, algorithmic bias, scalability, deep learning, real-time data processing.
How to cite this paper
@article{1706716,
author = {Hari Gupta, Dr Sangeet Vashishtha},
title = {Machine Learning in User Engagement: Engineering Solutions for Social Media Platforms},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {5},
pages = {766-797},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1706716.pdf},
abstract = {In recent years, the integration of machine learning (ML) techniques has significantly transformed user engagement strategies on social media platforms. These platforms rely heavily on personalized experiences to drive user interaction, retention, and overall platform growth. This paper explores the role of machine learning in enhancing user engagement, focusing on the engineering solutions that underpin recommendation systems, sentiment analysis, and behavior prediction models. By utilizing data-driven insights, ML algorithms enable platforms to tailor content, advertisements, and social interactions to individual users, thus fostering a deeper connection with the platform. Moreover, the paper delves into the challenges faced in implementing ML solutions, including data privacy concerns, algorithmic biases, and scalability issues. The study also presents case examples from leading social media companies, illustrating the practical applications of ML in improving user experience and platform dynamics. Finally, future directions in the field, such as the integration of advanced deep learning models and real-time data processing, are discussed to highlight emerging trends in the engineering of user engagement strategies for social media platforms.},
keywords = {Machine learning, user engagement, social media platforms, recommendation systems, sentiment analysis, behavior prediction, personalized content, data privacy, algorithmic bias, scalability, deep learning, real-time data processing.},
month = {November},
}