LightGCL: Simple yet effective graph contrastive learning for recommendation
Graph neural network (GNN) is a powerful learning approach for graph-based recommender
systems. Recently, GNNs integrated with contrastive learning have shown superior …
systems. Recently, GNNs integrated with contrastive learning have shown superior …
User Behavior Modeling with Deep Learning for Recommendation: Recent Advances
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 …
extensively used in recommender systems. The exploration of key interactive patterns …
Graph masked autoencoder for sequential recommendation
While some powerful neural network architectures (eg, Transformer, Graph Neural
Networks) have achieved improved performance in sequential recommendation with high …
Networks) have achieved improved performance in sequential recommendation with high …
Contrastive self-supervised learning in recommender systems: A survey
Deep learning-based recommender systems have achieved remarkable success in recent
years. However, these methods usually heavily rely on labeled data (ie, user-item …
years. However, these methods usually heavily rely on labeled data (ie, user-item …
Multimodal pre-training framework for sequential recommendation via contrastive learning
Current multimodal sequential recommendation models are often unable to effectively
explore and capture correlations among behavior sequences of users and items across …
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
The sequential recommendation system has been widely studied for its promising
effectiveness in capturing dynamic preferences buried in users' sequential behaviors …
effectiveness in capturing dynamic preferences buried in users' sequential behaviors …
Dynamic multi-objective optimization framework with interactive evolution for sequential recommendation
In contrast to traditional recommender systems which usually pay attention to users' general
and long-term preferences, sequential recommendation (SR) can model users' dynamic …
and long-term preferences, sequential recommendation (SR) can model users' dynamic …
Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation
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 …
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
Multimedia recommendation aims to fuse the multi-modal information of items for feature
enrichment to improve the recommendation performance. However, existing methods …
enrichment to improve the recommendation performance. However, existing methods …
End-to-end learnable clustering for intent learning in recommendation
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 …
recommendation, has become a hot research spot in recent years. However, the existing …