Design and Implementation of Attention-Based CR System in the Context of Big Data

The recommendation accuracy of traditional product recommendation systems is insufficient. Therefore, an improved deep factor decomposition machine algorithm combining adaptive regularization and attention mechanisms is proposed, and big data components are integrated to enable the algorithm to supp...

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Main Authors: Zheyi Wang, Yanli Kuang, Xin Lyu
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10473998/
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author Zheyi Wang
Yanli Kuang
Xin Lyu
author_facet Zheyi Wang
Yanli Kuang
Xin Lyu
author_sort Zheyi Wang
collection DOAJ
description The recommendation accuracy of traditional product recommendation systems is insufficient. Therefore, an improved deep factor decomposition machine algorithm combining adaptive regularization and attention mechanisms is proposed, and big data components are integrated to enable the algorithm to support more data input types. The publicly available datasets Criteo and Avazu from the Kaggle competition were selected for testing experiments in the study. The experimental results are as follows. During the training phase, after the convergence of each model, the loss function value of the model designed in this study is 1.26, which is the lowest among all comparison models. Moreover, when the number of recommended products is 7, the overall recommendation effect of each model is the best. The area under the curve of the subject operation characteristic curve of the model designed in this study on the Criteo dataset is 0.809, which is significantly higher than all comparison models. It is believed that this model has higher recommendation accuracy and can be used in application scenarios that require higher recommendation quality.
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issn 2169-3536
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publishDate 2024-01-01
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spelling doaj-art-a47d2c03f13349d4b88e241bfc0470b12025-01-28T00:01:02ZengIEEEIEEE Access2169-35362024-01-0112586395865010.1109/ACCESS.2024.337852110473998Design and Implementation of Attention-Based CR System in the Context of Big DataZheyi Wang0https://orcid.org/0009-0008-0068-3717Yanli Kuang1Xin Lyu2The University of Hong Kong, Hong Kong, ChinaImperial College London, London, U.K.Chongqing University of Posts and Telecommunications, Chongqing, ChinaThe recommendation accuracy of traditional product recommendation systems is insufficient. Therefore, an improved deep factor decomposition machine algorithm combining adaptive regularization and attention mechanisms is proposed, and big data components are integrated to enable the algorithm to support more data input types. The publicly available datasets Criteo and Avazu from the Kaggle competition were selected for testing experiments in the study. The experimental results are as follows. During the training phase, after the convergence of each model, the loss function value of the model designed in this study is 1.26, which is the lowest among all comparison models. Moreover, when the number of recommended products is 7, the overall recommendation effect of each model is the best. The area under the curve of the subject operation characteristic curve of the model designed in this study on the Criteo dataset is 0.809, which is significantly higher than all comparison models. It is believed that this model has higher recommendation accuracy and can be used in application scenarios that require higher recommendation quality.https://ieeexplore.ieee.org/document/10473998/Big dataattention mechanismscommodity recommendationsneural networksadaptive regularit
spellingShingle Zheyi Wang
Yanli Kuang
Xin Lyu
Design and Implementation of Attention-Based CR System in the Context of Big Data
IEEE Access
Big data
attention mechanisms
commodity recommendations
neural networks
adaptive regularit
title Design and Implementation of Attention-Based CR System in the Context of Big Data
title_full Design and Implementation of Attention-Based CR System in the Context of Big Data
title_fullStr Design and Implementation of Attention-Based CR System in the Context of Big Data
title_full_unstemmed Design and Implementation of Attention-Based CR System in the Context of Big Data
title_short Design and Implementation of Attention-Based CR System in the Context of Big Data
title_sort design and implementation of attention based cr system in the context of big data
topic Big data
attention mechanisms
commodity recommendations
neural networks
adaptive regularit
url https://ieeexplore.ieee.org/document/10473998/
work_keys_str_mv AT zheyiwang designandimplementationofattentionbasedcrsysteminthecontextofbigdata
AT yanlikuang designandimplementationofattentionbasedcrsysteminthecontextofbigdata
AT xinlyu designandimplementationofattentionbasedcrsysteminthecontextofbigdata