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Contrastive learning for representation degeneration problem in sequential recommendation
Recent advancements of sequential deep learning models such as Transformer and BERT
have significantly facilitated the sequential recommendation. However, according to our …
have significantly facilitated the sequential recommendation. However, according to our …
Disentangled representation learning
Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …
and disentangling the underlying factors hidden in the observable data in representation …
Self-supervised hypergraph convolutional networks for session-based recommendation
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 …
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
Social relations are often used to improve recommendation quality when user-item
interaction data is sparse in recommender systems. Most existing social recommendation …
interaction data is sparse in recommender systems. Most existing social recommendation …
Contrastive self-supervised sequential recommendation with robust augmentation
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 …
in order to predict future interactions in sequential user data. At their core, such approaches …