61 citations · 85 across the 3 of their papers we have counts for
3 papers
cs.IR2024★ 24 cited
Embedding Compression in Recommender Systems: A Survey
Shiwei Li, Huifeng Guo, Xing Tang +4
To alleviate the problem of information explosion, recommender systems are widely deployed to provide personalized information filtering services. Usually, embedding tables are emp…
cs.IR2024★ 61 cited
RecDCL: Dual Contrastive Learning for Recommendation
Dan Zhang, Yangliao Geng, Wenwen Gong +6
Self-supervised learning (SSL) has recently achieved great success in mining the user-item interactions for collaborative filtering. As a major paradigm, contrastive learning (CL)…
cs.IR2023
Towards Automated Negative Sampling in Implicit Recommendation
Fuyuan Lyu, Yaochen Hu, Xing Tang +3
Negative sampling methods are vital in implicit recommendation models as they allow us to obtain negative instances from massive unlabeled data. Most existing approaches focus on s…