Graph neural networks in recommender systems: a survey
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 graph co-training for session-based recommendation
Session-based recommendation targets next-item prediction by exploiting user behaviors
within a short time period. Compared with other recommendation paradigms, session-based …
within a short time period. Compared with other recommendation paradigms, session-based …
Continuous-time sequential recommendation with temporal graph collaborative transformer
In order to model the evolution of user preference, we should learn user/item embeddings
based on time-ordered item purchasing sequences, which is defined as Sequential …
based on time-ordered item purchasing sequences, which is defined as Sequential …