Machine Learning Algorithmic Approaches to Maximizing User Engagement through Ad Placements

Authors

  • Shashishekhar Ramagundam  India

DOI:

https://doi.org/10.32628/CSEIT2063238

Keywords:

Click-Through Rates Clustering, Machine Learning, Reinforcement Learning, Segmentation, User Retention, User Engagement.

Abstract

The exponential growth of digital advertising across online platforms has intensified the need for effective ad placement strategies aimed at maximizing user engagement. Traditional ad placement methods, relying on heuristic rules and demographic targeting, have shown limitations in their ability to adapt to dynamic user behaviors. This paper proposes a machine learning-based approach to optimize ad placement, focusing on three main techniques: collaborative filtering, reinforcement learning, and clustering. Our model evaluates the impact of these techniques on engagement metrics such as click-through rates (CTR), user retention, and time spent on ads. Results show that the machine learning model significantly outperforms traditional methods, with a 25% increase in CTR, 30% improvement in engagement duration, and a 20% increase in segmentation performance. These findings underscore the potential of integrating machine learning to enhance the personalization, adaptability, and overall effectiveness of digital ad placements, offering substantial gains in user interaction and engagement.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-06-30

Issue

Volume 6, Issue 3, May-June-2020

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Shashishekhar Ramagundam, " Machine Learning Algorithmic Approaches to Maximizing User Engagement through Ad Placements " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 3, pp.1164-1174, May-June-2020. Available at doi : https://doi.org/10.32628/CSEIT2063238