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22 February 2023 Click-through rate prediction based on behavioral sequences
Shoujian Yu, Xiaoxiao Huang, Xiaoling Xia
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Proceedings Volume 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022); 1258711 (2023) https://doi.org/10.1117/12.2667249
Event: Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 2022, Shanghai, China
Abstract
As the most important module in recommendation systems, click-through rate prediction has attracted the attention of industry and academia. Due to the powerful learning ability of deep learning, it is widely used in click-through rate prediction. Behavior sequences based on user is an important direction of click-through rate prediction. Although some results have been made in related directions, existing methods still have some problems, such as the inability to learn feature weights better, the presence of noise in user behavior sequences, not fully mining the hidden information in features, etc. In this paper, we propose a method for related problems, named DISFMN, which can dynamically learn the importance of features as well as filter out the noise in user behavior sequences. The method also combines high-order and low-order feature interactions to uncover more valuable information in features. Comparative experiments are conducted on different datasets and the experimental results showed the effectiveness of the proposed method.
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Shoujian Yu, Xiaoxiao Huang, and Xiaoling Xia "Click-through rate prediction based on behavioral sequences", Proc. SPIE 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 1258711 (22 February 2023); https://doi.org/10.1117/12.2667249
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KEYWORDS
Data modeling
Ablation
Deep learning
Neural networks
Technology
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