A neural collaborative filtering recommendation algorithm based on attention mechanism and contrastive learning
The neural collaborative filtering recommendation algorithm widely serves as personalized recommendations of users, which further applies deep learning to a recommendation system. It is a universal framework in the neural collaborative filtering recommendation algorithm; however, it does not regard...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-07-01
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| Series: | Nonlinear Engineering |
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| Online Access: | https://doi.org/10.1515/nleng-2025-0137 |
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| _version_ | 1849715408800579584 |
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| author | Liu Jianqiao |
| author_facet | Liu Jianqiao |
| author_sort | Liu Jianqiao |
| collection | DOAJ |
| description | The neural collaborative filtering recommendation algorithm widely serves as personalized recommendations of users, which further applies deep learning to a recommendation system. It is a universal framework in the neural collaborative filtering recommendation algorithm; however, it does not regard the impact of important features on recommendation results, nor does it regard the issues of data sparsity and long tail distribution of items. To settle these issues, this article proposes a recommendation algorithm based on the attention mechanism and contrastive learning, which focuses on more important features through the attention mechanism and increases the quantity of samples to achieve data augmentation through contrastive learning; therefore, it enhances recommendation performance. The experimental results on two benchmark datasets show that the algorithm proposed in this article has further enhanced recommendation performance compared to other benchmark algorithms. |
| format | Article |
| id | doaj-art-b3f925a5fba64d4c8689fc96896f8fa5 |
| institution | DOAJ |
| issn | 2192-8029 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nonlinear Engineering |
| spelling | doaj-art-b3f925a5fba64d4c8689fc96896f8fa52025-08-20T03:13:23ZengDe GruyterNonlinear Engineering2192-80292025-07-01141p. 1910.1515/nleng-2025-0137A neural collaborative filtering recommendation algorithm based on attention mechanism and contrastive learningLiu Jianqiao0School of Foreign Languages, Fuyang Normal University, Fuyang, Anhui, 236037, ChinaThe neural collaborative filtering recommendation algorithm widely serves as personalized recommendations of users, which further applies deep learning to a recommendation system. It is a universal framework in the neural collaborative filtering recommendation algorithm; however, it does not regard the impact of important features on recommendation results, nor does it regard the issues of data sparsity and long tail distribution of items. To settle these issues, this article proposes a recommendation algorithm based on the attention mechanism and contrastive learning, which focuses on more important features through the attention mechanism and increases the quantity of samples to achieve data augmentation through contrastive learning; therefore, it enhances recommendation performance. The experimental results on two benchmark datasets show that the algorithm proposed in this article has further enhanced recommendation performance compared to other benchmark algorithms.https://doi.org/10.1515/nleng-2025-0137attention mechanismcontrastive learningdeep learningneural collaborative filteringrecommendation algorithm |
| spellingShingle | Liu Jianqiao A neural collaborative filtering recommendation algorithm based on attention mechanism and contrastive learning Nonlinear Engineering attention mechanism contrastive learning deep learning neural collaborative filtering recommendation algorithm |
| title | A neural collaborative filtering recommendation algorithm based on attention mechanism and contrastive learning |
| title_full | A neural collaborative filtering recommendation algorithm based on attention mechanism and contrastive learning |
| title_fullStr | A neural collaborative filtering recommendation algorithm based on attention mechanism and contrastive learning |
| title_full_unstemmed | A neural collaborative filtering recommendation algorithm based on attention mechanism and contrastive learning |
| title_short | A neural collaborative filtering recommendation algorithm based on attention mechanism and contrastive learning |
| title_sort | neural collaborative filtering recommendation algorithm based on attention mechanism and contrastive learning |
| topic | attention mechanism contrastive learning deep learning neural collaborative filtering recommendation algorithm |
| url | https://doi.org/10.1515/nleng-2025-0137 |
| work_keys_str_mv | AT liujianqiao aneuralcollaborativefilteringrecommendationalgorithmbasedonattentionmechanismandcontrastivelearning AT liujianqiao neuralcollaborativefilteringrecommendationalgorithmbasedonattentionmechanismandcontrastivelearning |