Contrastive learning for representation degeneration problem in sequential recommendation

R Qiu, Z Huang, H Yin, Z Wang - … conference on web search and data …, 2022‏ - dl.acm.org
Recent advancements of sequential deep learning models such as Transformer and BERT
have significantly facilitated the sequential recommendation. However, according to our …

Disentangled representation learning

X Wang, H Chen, Z Wu, W Zhu - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …

Self-supervised hypergraph convolutional networks for session-based recommendation

X **a, H Yin, J Yu, Q Wang, L Cui… - Proceedings of the AAAI …, 2021‏ - ojs.aaai.org
Session-based recommendation (SBR) focuses on next-item prediction at a certain time
point. As user profiles are generally not available in this scenario, capturing the user intent …

Self-supervised multi-channel hypergraph convolutional network for social recommendation

J Yu, H Yin, J Li, Q Wang, NQV Hung… - Proceedings of the web …, 2021‏ - dl.acm.org
Social relations are often used to improve recommendation quality when user-item
interaction data is sparse in recommender systems. Most existing social recommendation …

Contrastive self-supervised sequential recommendation with robust augmentation

Z Liu, Y Chen, J Li, PS Yu, J McAuley… - arxiv preprint arxiv …, 2021‏ - arxiv.org
Sequential Recommendationdescribes a set of techniques to model dynamic user behavior
in order to predict future interactions in sequential user data. At their core, such approaches …