LightGCL: Simple yet effective graph contrastive learning for recommendation

X Cai, C Huang, L Xia, X Ren - arXiv preprint arXiv:2302.08191, 2023 - arxiv.org
Graph neural network (GNN) is a powerful learning approach for graph-based recommender
systems. Recently, GNNs integrated with contrastive learning have shown superior …

User Behavior Modeling with Deep Learning for Recommendation: Recent Advances

W Liu, W Guo, Y Liu, R Tang, H Wang - … of the 17th ACM Conference on …, 2023 - dl.acm.org
User Behavior Modeling (UBM) plays a critical role in user interest learning, and has been
extensively used in recommender systems. The exploration of key interactive patterns …

Graph masked autoencoder for sequential recommendation

Y Ye, L Xia, C Huang - Proceedings of the 46th International ACM SIGIR …, 2023 - dl.acm.org
While some powerful neural network architectures (eg, Transformer, Graph Neural
Networks) have achieved improved performance in sequential recommendation with high …

Contrastive self-supervised learning in recommender systems: A survey

M Jing, Y Zhu, T Zang, K Wang - ACM Transactions on Information …, 2023 - dl.acm.org
Deep learning-based recommender systems have achieved remarkable success in recent
years. However, these methods usually heavily rely on labeled data (ie, user-item …

Multimodal pre-training framework for sequential recommendation via contrastive learning

L Zhang, X Zhou, Z Zeng, Z Shen - arXiv preprint arXiv:2303.11879, 2023 - arxiv.org
Current multimodal sequential recommendation models are often unable to effectively
explore and capture correlations among behavior sequences of users and items across …

APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation

M Yin, H Wang, X Xu, L Wu, S Zhao, W Guo… - Proceedings of the …, 2023 - dl.acm.org
The sequential recommendation system has been widely studied for its promising
effectiveness in capturing dynamic preferences buried in users' sequential behaviors …

Dynamic multi-objective optimization framework with interactive evolution for sequential recommendation

W Zhou, Y Liu, M Li, Y Wang, Z Shen… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In contrast to traditional recommender systems which usually pay attention to users' general
and long-term preferences, sequential recommendation (SR) can model users' dynamic …

Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation

X Qin, H Yuan, P Zhao, G Liu, F Zhuang… - Proceedings of the 17th …, 2024 - dl.acm.org
The user purchase behaviors are mainly influenced by their intentions (eg, buying clothes
for decoration, buying brushes for painting, etc.). Modeling a user's latent intention can …

Cross-modal content inference and feature enrichment for cold-start recommendation

H Ma, Z Qi, X Dong, X Li, Y Zheng… - … Joint Conference on …, 2023 - ieeexplore.ieee.org
Multimedia recommendation aims to fuse the multi-modal information of items for feature
enrichment to improve the recommendation performance. However, existing methods …

End-to-end learnable clustering for intent learning in recommendation

Y Liu, S Zhu, J Xia, Y Ma, J Ma, W Zhong, X Liu… - arXiv preprint arXiv …, 2024 - arxiv.org
Intent learning, which aims to learn users' intents for user understanding and item
recommendation, has become a hot research spot in recent years. However, the existing …