FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network
In recommendation models, bias can distort the distribution of user-generated data, leading to inaccurate representation of user preferences. Failure to filter out biased data can result in significant learning errors, ultimately reducing the accuracy of the recommendation model. To address this iss...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/13/7975 |
| _version_ | 1797592087020961792 |
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| author | Zhaoxuan Liu Wenjie Luo |
| author_facet | Zhaoxuan Liu Wenjie Luo |
| author_sort | Zhaoxuan Liu |
| collection | DOAJ |
| description | In recommendation models, bias can distort the distribution of user-generated data, leading to inaccurate representation of user preferences. Failure to filter out biased data can result in significant learning errors, ultimately reducing the accuracy of the recommendation model. To address this issue, this paper proposes a Generative Adversarial Network (GAN) model comprising a filter-enhanced Multi-Layer Perceptron (MLP) generator and a linear discriminator to mitigate bias and improve the accuracy of the recommendation. The proposed model leverages the GAN architecture, where the filter structure in the generator enhances the data distribution before model training, allowing for the generation of more precise recommendation lists. The discriminator learns from the skew-corrected user review list to extract user features, which are then used alongside the recommendation list generated by G in an adversarial process. This adversarial process enables each component to optimize and improve itself while strengthening the correction effect. To enhance the accuracy of G generation, we evaluate the influence of three different input lists on the filter effect. Finally, we validate our model on two real-world datasets by comparing the effect of filter-augmented MLP and pure MLP generators. Our results demonstrate the effectiveness of filters, and our model achieves better recommendation accuracy than other baseline models. |
| first_indexed | 2024-03-11T01:46:27Z |
| format | Article |
| id | doaj.art-bb37dc897050411e91fdd115e1230c64 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:46:27Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-bb37dc897050411e91fdd115e1230c642023-11-18T16:14:09ZengMDPI AGApplied Sciences2076-34172023-07-011313797510.3390/app13137975FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial NetworkZhaoxuan Liu0Wenjie Luo1School of Cybersecurity and Computer, Hebei University, Baoding 071002, ChinaSchool of Cybersecurity and Computer, Hebei University, Baoding 071002, ChinaIn recommendation models, bias can distort the distribution of user-generated data, leading to inaccurate representation of user preferences. Failure to filter out biased data can result in significant learning errors, ultimately reducing the accuracy of the recommendation model. To address this issue, this paper proposes a Generative Adversarial Network (GAN) model comprising a filter-enhanced Multi-Layer Perceptron (MLP) generator and a linear discriminator to mitigate bias and improve the accuracy of the recommendation. The proposed model leverages the GAN architecture, where the filter structure in the generator enhances the data distribution before model training, allowing for the generation of more precise recommendation lists. The discriminator learns from the skew-corrected user review list to extract user features, which are then used alongside the recommendation list generated by G in an adversarial process. This adversarial process enables each component to optimize and improve itself while strengthening the correction effect. To enhance the accuracy of G generation, we evaluate the influence of three different input lists on the filter effect. Finally, we validate our model on two real-world datasets by comparing the effect of filter-augmented MLP and pure MLP generators. Our results demonstrate the effectiveness of filters, and our model achieves better recommendation accuracy than other baseline models.https://www.mdpi.com/2076-3417/13/13/7975top-N recommendationdebiasfilter MLPgenerative adversarial networks |
| spellingShingle | Zhaoxuan Liu Wenjie Luo FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network Applied Sciences top-N recommendation debias filter MLP generative adversarial networks |
| title | FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network |
| title_full | FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network |
| title_fullStr | FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network |
| title_full_unstemmed | FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network |
| title_short | FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network |
| title_sort | fmgan a filter enhanced mlp debias recommendation model based on generative adversarial network |
| topic | top-N recommendation debias filter MLP generative adversarial networks |
| url | https://www.mdpi.com/2076-3417/13/13/7975 |
| work_keys_str_mv | AT zhaoxuanliu fmganafilterenhancedmlpdebiasrecommendationmodelbasedongenerativeadversarialnetwork AT wenjieluo fmganafilterenhancedmlpdebiasrecommendationmodelbasedongenerativeadversarialnetwork |