Contrastive learning for recommendation

Author: mgls

August undefined, 2024

WebMar 2, 2024 · Recently, contrastive self-supervised learning has become successful in recommendation. In light of this, we propose a Heterogeneous Graph Contrastive … WebJul 23, 2024 · Graph Convolution Network (GCN) has been applied in recommendation with various architectures for its representation learning capability in graph-structured …

Data Intelligence Lab@HKU - Google Sites

WebMay 1, 2024 · As self-supervised learning (SSL) has proven to be effective in the field of recommender systems [1,23,29,32], researchers have sought to leverage this paradigm … WebContrastive Learning for Cold-Start Recommendation. In Proceedings of MM. Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner, and Himanshu Rai. 2024. Noise … brave bear directed drawing

Self-guided Contrastive Learning for Sequential Recommendation

WebI am an Assistant Professor at the Department of Computer Science in the University of Hong Kong (HKU). I am the director of Data Intelligence Lab@HKU, with the focus on developing novel machine learning frameworks to tackle various challenges in Data Mining, Information Retrieval, Spatial-Temporal Data Analytics, User Behavior Modeling, … WebSep 3, 2024 · Abstract: Recently, contrastive learning has been applied to the sequential recommendation task to address data sparsity caused by users with … WebDec 22, 2024 · The learning paradigm of SCL applies a multi-task learning strategy to jointly optimize the recommendation task loss(BPR loss, formula (5)) and the supervised contrastive loss(S-InfoNCE loss, formula (11)): (12) L = L B P R + λ 1 L S − I n f o N C E + λ 2 L 2 where λ 1 is the hyperparameters to control the strengths of S-InfoNCE. brave bear cubs

Contrastive Learning for Cold-Start Recommendation

WebHere, we propose a Contrastive Graph Structure Learning via Information Bottleneck (CGI) for recommendation, which adaptively learns whether to drop an edge or node to obtain optimized graph structures in an end-to-end manner. Moreover, we innovatively introduce the Information Bottleneck into the contrastive learning process to avoid capturing ... Webcontrastive learning utilizes IB performing on graph representations as the unsupervised loss. Both Yu et al. [40] and Yu et al. [42] aim to directly reveal the vital substructure in the subgraph level, ... On the other hand, the vanilla randomly dropout in most contrastive learning for recommendation cannot create powerful views to alleviate ... brave bear disneyWebApr 14, 2024 · 2.3 Graph Contrastive Learning in Recommendation. To overcome the data sparsity issues, Graph Contrastive Learning (GCL) is introduced into … brave bear bow

"WebExisting GNN and contrastive learning based recommendation models learn user and item representations in a symmetrical way, and utilize social information and contrastive learning in a complex manner. Above two strategies result in that these models are either ineffective for datasets with a serious imbalance between users and items or ... " - Contrastive learning for recommendation

Data Intelligence Lab@HKU - Google Sites

Self-guided Contrastive Learning for Sequential Recommendation

Contrastive learning for recommendation

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