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A Dual Adaptive Interaction Click-Through Rate Prediction Based on Attention Logarithmic Interaction Network

Click-through rate (CTR) prediction is crucial for computing advertisement and recommender systems. The key challenge of CTR prediction is to accurately capture user interests and deliver suitable advertisements to the right people. However, there are an immense number of features in CTR prediction...

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Autores principales: Li, Shiqi, Cui, Zhendong, Pei, Yongquan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778598/
https://www.ncbi.nlm.nih.gov/pubmed/36554236
http://dx.doi.org/10.3390/e24121831
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author Li, Shiqi
Cui, Zhendong
Pei, Yongquan
author_facet Li, Shiqi
Cui, Zhendong
Pei, Yongquan
author_sort Li, Shiqi
collection PubMed
description Click-through rate (CTR) prediction is crucial for computing advertisement and recommender systems. The key challenge of CTR prediction is to accurately capture user interests and deliver suitable advertisements to the right people. However, there are an immense number of features in CTR prediction datasets, which hardly fit when only using an individual feature. To solve this problem, feature interaction that combines several features via an operation is introduced to enhance prediction performance. Many factorizations machine-based models and deep learning methods have been proposed to capture feature interaction for CTR prediction. They follow an enumeration-filter pattern that could not determine the appropriate order of feature interaction and useful feature interaction. The attention logarithmic network (ALN) is presented in this paper, which uses logarithmic neural networks (LNN) to model feature interactions, and the squeeze excitation (SE) mechanism to adaptively model the importance of higher-order feature interactions. At first, the embedding vector of the input was absolutized and a very small positive number was added to the zeros of the embedding vector, which made the LNN input positive. Then, the adaptive-order feature interactions were learned by logarithmic transformation and exponential transformation in the LNN. Finally, SE was applied to model the importance of high-order feature interactions adaptively for enhancing CTR performance. Based on this, the attention logarithmic interaction network (ALIN) was proposed for the effectiveness and accuracy of CTR, which integrated Newton’s identity into ALN. ALIN supplements the loss of information, which is caused by the operation becoming positive and by adding a small positive value to the embedding vector. Experiments are conducted on two datasets, and the results prove that ALIN is efficient and effective.
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spelling pubmed-97785982022-12-23 A Dual Adaptive Interaction Click-Through Rate Prediction Based on Attention Logarithmic Interaction Network Li, Shiqi Cui, Zhendong Pei, Yongquan Entropy (Basel) Article Click-through rate (CTR) prediction is crucial for computing advertisement and recommender systems. The key challenge of CTR prediction is to accurately capture user interests and deliver suitable advertisements to the right people. However, there are an immense number of features in CTR prediction datasets, which hardly fit when only using an individual feature. To solve this problem, feature interaction that combines several features via an operation is introduced to enhance prediction performance. Many factorizations machine-based models and deep learning methods have been proposed to capture feature interaction for CTR prediction. They follow an enumeration-filter pattern that could not determine the appropriate order of feature interaction and useful feature interaction. The attention logarithmic network (ALN) is presented in this paper, which uses logarithmic neural networks (LNN) to model feature interactions, and the squeeze excitation (SE) mechanism to adaptively model the importance of higher-order feature interactions. At first, the embedding vector of the input was absolutized and a very small positive number was added to the zeros of the embedding vector, which made the LNN input positive. Then, the adaptive-order feature interactions were learned by logarithmic transformation and exponential transformation in the LNN. Finally, SE was applied to model the importance of high-order feature interactions adaptively for enhancing CTR performance. Based on this, the attention logarithmic interaction network (ALIN) was proposed for the effectiveness and accuracy of CTR, which integrated Newton’s identity into ALN. ALIN supplements the loss of information, which is caused by the operation becoming positive and by adding a small positive value to the embedding vector. Experiments are conducted on two datasets, and the results prove that ALIN is efficient and effective. MDPI 2022-12-15 /pmc/articles/PMC9778598/ /pubmed/36554236 http://dx.doi.org/10.3390/e24121831 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Shiqi
Cui, Zhendong
Pei, Yongquan
A Dual Adaptive Interaction Click-Through Rate Prediction Based on Attention Logarithmic Interaction Network
title A Dual Adaptive Interaction Click-Through Rate Prediction Based on Attention Logarithmic Interaction Network
title_full A Dual Adaptive Interaction Click-Through Rate Prediction Based on Attention Logarithmic Interaction Network
title_fullStr A Dual Adaptive Interaction Click-Through Rate Prediction Based on Attention Logarithmic Interaction Network
title_full_unstemmed A Dual Adaptive Interaction Click-Through Rate Prediction Based on Attention Logarithmic Interaction Network
title_short A Dual Adaptive Interaction Click-Through Rate Prediction Based on Attention Logarithmic Interaction Network
title_sort dual adaptive interaction click-through rate prediction based on attention logarithmic interaction network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778598/
https://www.ncbi.nlm.nih.gov/pubmed/36554236
http://dx.doi.org/10.3390/e24121831
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