Contrastive Learning-Based Personalized Tag Recommendation
Personalized tag recommendation algorithms generate personalized tag lists for users by learning the tagging preferences of users. Traditional personalized tag recommendation systems are limited by the problem of data sparsity, making the personalized tag recommendation models unable to accurately l...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/18/6061 |
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| author | Aoran Zhang Yonghong Yu Shenglong Li Rong Gao Li Zhang Shang Gao |
| author_facet | Aoran Zhang Yonghong Yu Shenglong Li Rong Gao Li Zhang Shang Gao |
| author_sort | Aoran Zhang |
| collection | DOAJ |
| description | Personalized tag recommendation algorithms generate personalized tag lists for users by learning the tagging preferences of users. Traditional personalized tag recommendation systems are limited by the problem of data sparsity, making the personalized tag recommendation models unable to accurately learn the embeddings of users, items, and tags. To address this issue, we propose a contrastive learning-based personalized tag recommendation algorithm, namely CLPTR. Specifically, CLPTR generates augmented views of user–tag and item–tag interaction graphs by injecting noises into implicit feature representations rather than dropping nodes and edges. Hence, CLPTR is able to greatly preserve the underlying semantics of the original user–tag or the item–tag interaction graphs and avoid destroying their structural information. In addition, we integrate the contrastive learning module into a graph neural network-based personalized tag recommendation model, which enables the model to extract self-supervised signals from user–tag and item–tag interaction graphs. We conduct extensive experiments on real-world datasets, and the experimental results demonstrate the state-of-the-art performance of our proposed CLPTR compared with traditional personalized tag recommendation models. |
| format | Article |
| id | doaj-art-ff3f093bf6e84bd38da28a3d779901d0 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ff3f093bf6e84bd38da28a3d779901d02025-08-20T01:55:51ZengMDPI AGSensors1424-82202024-09-012418606110.3390/s24186061Contrastive Learning-Based Personalized Tag RecommendationAoran Zhang0Yonghong Yu1Shenglong Li2Rong Gao3Li Zhang4Shang Gao5School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212000, ChinaCollege of Tongda, Nanjing University of Posts and Telecommunication, Yangzhou 225127, ChinaCollege of Tongda, Nanjing University of Posts and Telecommunication, Yangzhou 225127, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaDepartment of Computer Science, Royal Holloway University of London, Egham TW20 0EX, UKSchool of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212000, ChinaPersonalized tag recommendation algorithms generate personalized tag lists for users by learning the tagging preferences of users. Traditional personalized tag recommendation systems are limited by the problem of data sparsity, making the personalized tag recommendation models unable to accurately learn the embeddings of users, items, and tags. To address this issue, we propose a contrastive learning-based personalized tag recommendation algorithm, namely CLPTR. Specifically, CLPTR generates augmented views of user–tag and item–tag interaction graphs by injecting noises into implicit feature representations rather than dropping nodes and edges. Hence, CLPTR is able to greatly preserve the underlying semantics of the original user–tag or the item–tag interaction graphs and avoid destroying their structural information. In addition, we integrate the contrastive learning module into a graph neural network-based personalized tag recommendation model, which enables the model to extract self-supervised signals from user–tag and item–tag interaction graphs. We conduct extensive experiments on real-world datasets, and the experimental results demonstrate the state-of-the-art performance of our proposed CLPTR compared with traditional personalized tag recommendation models.https://www.mdpi.com/1424-8220/24/18/6061contrastive learninggraph neural networkpersonalized tag recommendation |
| spellingShingle | Aoran Zhang Yonghong Yu Shenglong Li Rong Gao Li Zhang Shang Gao Contrastive Learning-Based Personalized Tag Recommendation Sensors contrastive learning graph neural network personalized tag recommendation |
| title | Contrastive Learning-Based Personalized Tag Recommendation |
| title_full | Contrastive Learning-Based Personalized Tag Recommendation |
| title_fullStr | Contrastive Learning-Based Personalized Tag Recommendation |
| title_full_unstemmed | Contrastive Learning-Based Personalized Tag Recommendation |
| title_short | Contrastive Learning-Based Personalized Tag Recommendation |
| title_sort | contrastive learning based personalized tag recommendation |
| topic | contrastive learning graph neural network personalized tag recommendation |
| url | https://www.mdpi.com/1424-8220/24/18/6061 |
| work_keys_str_mv | AT aoranzhang contrastivelearningbasedpersonalizedtagrecommendation AT yonghongyu contrastivelearningbasedpersonalizedtagrecommendation AT shenglongli contrastivelearningbasedpersonalizedtagrecommendation AT ronggao contrastivelearningbasedpersonalizedtagrecommendation AT lizhang contrastivelearningbasedpersonalizedtagrecommendation AT shanggao contrastivelearningbasedpersonalizedtagrecommendation |