Contrastive self-supervised learning in recommender systems: A survey
Deep learning-based recommender systems have achieved remarkable success in recent
years. However, these methods usually heavily rely on labeled data (ie, user-item …
years. However, these methods usually heavily rely on labeled data (ie, user-item …
FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning
As trustworthy AI continues to advance, the fairness issue in recommendations has received
increasing attention. A recommender system is considered unfair when it produces unequal …
increasing attention. A recommender system is considered unfair when it produces unequal …
LMACL: improving graph collaborative filtering with learnable model augmentation contrastive learning
Graph collaborative filtering (GCF) has achieved exciting recommendation performance with
its ability to aggregate high-order graph structure information. Recently, contrastive learning …
its ability to aggregate high-order graph structure information. Recently, contrastive learning …
Graph Contrastive Learning with Kernel Dependence Maximization for Social Recommendation
Contrastive learning (CL) has recently catalyzed a productive avenue of research for
recommendation. The efficacy of most CL methods for recommendation may hinge on their …
recommendation. The efficacy of most CL methods for recommendation may hinge on their …
Popularity debiasing from exposure to interaction in collaborative filtering
Recommender systems often suffer from popularity bias, where popular items are overly
recommended while sacrificing unpopular items. Existing researches generally focus on …
recommended while sacrificing unpopular items. Existing researches generally focus on …
An improved cross-domain sequential recommendation model based on intra-domain and inter-domain contrastive learning
J Ni, T Shen, Y Zhao, G Tang, Y Gu - Complex & Intelligent Systems, 2024 - Springer
Cross-domain recommendation aims to integrate data from multiple domains and introduce
information from source domains, thereby achieving good recommendations on the target …
information from source domains, thereby achieving good recommendations on the target …
An unsupervised multi-view contrastive learning framework with attention-based reranking strategy for entity alignment
Y Liang, W Cai, M Yang, Y Jiang - Neural Networks, 2024 - Elsevier
Entity alignment is a crucial task in knowledge graphs, aiming to match corresponding
entities from different knowledge graphs. Due to the scarcity of pre-aligned entities in real …
entities from different knowledge graphs. Due to the scarcity of pre-aligned entities in real …
Future Augmentation with Self-distillation in Recommendation
Sequential recommendation (SR) aims to provide appropriate items a user will click
according to the user's historical behavior sequence. Conventional SR models are trained …
according to the user's historical behavior sequence. Conventional SR models are trained …
Metric learning with adversarial hard negative samples for tag recommendation
J Wang, G Chen, K Xin, Z Fei - The Journal of Supercomputing, 2024 - Springer
Tag recommendation can suggest a collection of tags to users and effectively describe the
characteristics of resources or users' preferences, thereby enhancing the accuracy of …
characteristics of resources or users' preferences, thereby enhancing the accuracy of …
Dual Adversarial Perturbators Generate rich Views for Recommendation
L Zhang, Y Yao, H Ye - arXiv preprint arXiv:2409.06719, 2024 - arxiv.org
Graph contrastive learning (GCL) has been extensively studied and leveraged as a potent
tool in recommender systems. Most existing GCL-based recommenders generate …
tool in recommender systems. Most existing GCL-based recommenders generate …