Dynamic Attention-Based Click-Through Rate Prediction Model
Authors: 
Zhiwei Ma, Ying DongAuthors Info & Claims
Zhiwei Ma, Ying DongAuthors Info & ClaimsArticle No.: 10, Pages 1 - 6
Abstract
Many online firms rely significantly on marketing and subscription rankings, such as Tencent and TikTok. CTR prediction is Critical in this area, but existing methods frequently underestimate the impact of feature interactions. To overcome this issue, a unique CTR model based on attention processes is presented. The goal of this approach is to represent the dynamic interactions between characteristics and their contextual importance. It does this through the employment of a global perception module, which defines overarching features, and a local perception module, which concentrates on finer-grained component dynamics. These perceived characteristics are then blended. Experiments on two publicly available datasets reveal that this strategy beats benchmarks designs, with a 0.65% AUC increase on the Avazu database and a 0.25% AUC gain on the Criteo dataset, confirming its efficacy in CTR prediction.
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Published In
September 2023
133 pages
ISBN:9798400708312
DOI:10.1145/3625343
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 11 November 2023
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AIMLR 2023
AIMLR 2023: 2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics
September 15 - 17, 2023
Bangkok, Thailand
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