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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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-09, Vol.24 (18), p.6061
Main Authors: Zhang, Aoran, Yu, Yonghong, Li, Shenglong, Gao, Rong, Zhang, Li, Gao, Shang
Format: Article
Language:English
Subjects:
Acids
Algorithms
Collaboration
Computational linguistics
contrastive learning
Deep learning
graph neural network
Graphs
Hydrocarbons
Language processing
Natural language interfaces
Neural networks
personalized tag recommendation
Recommender systems
Semantics
Sparsity
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24186061