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<p>Click-through rate prediction (CTR) remains a significant research topic, which is crucial for online advertising and recommendation systems. Despite numerous advantages in CTR models, performance improvements have been limited, which lack contextual learning abilities and are prone to over fitting challenges. Therefore, to overcome such limitations, this research introduces a Modified Frequency Wise Hessian Eigen Value Regularization Enabled Bidirectional Long Short-Term Memory framework (MFHESTM), which reveals a strong positive correlation between the feature frequency and top Hessian eigenvalue. The proposed MFHESTM framework leverages the benefits of fractional calculus into the stochastic gradient descent, which allows for more nuanced updates by considering the memory effect of fractional derivatives. When compared with the prevailing methods, the empirical results exhibit that the proposed model shows superior prediction performance in terms of accuracy of 98.28%, specificity of 98.24% sensitivity of 98.31%, and Log-loss of 2.36 for the Avazu dataset. The proposed framework can be used by advertisers to optimize their campaigns, enhance ad-clicks traffic and allocate resources in an efficient manner.</p>

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A novel and optimized deep learning model for click through rate prediction

  • 期刊论文

  • 发表日期：

    2025年8月20日

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  • Piyush Gupta,
  • Komal Kumar Bhatia,
  • Neelam Duhan

摘要

Click-through rate prediction (CTR) remains a significant research topic, which is crucial for online advertising and recommendation systems. Despite numerous advantages in CTR models, performance improvements have been limited, which lack contextual learning abilities and are prone to over fitting challenges. Therefore, to overcome such limitations, this research introduces a Modified Frequency Wise Hessian Eigen Value Regularization Enabled Bidirectional Long Short-Term Memory framework (MFHESTM), which reveals a strong positive correlation between the feature frequency and top Hessian eigenvalue. The proposed MFHESTM framework leverages the benefits of fractional calculus into the stochastic gradient descent, which allows for more nuanced updates by considering the memory effect of fractional derivatives. When compared with the prevailing methods, the empirical results exhibit that the proposed model shows superior prediction performance in terms of accuracy of 98.28%, specificity of 98.24% sensitivity of 98.31%, and Log-loss of 2.36 for the Avazu dataset. The proposed framework can be used by advertisers to optimize their campaigns, enhance ad-clicks traffic and allocate resources in an efficient manner.