Debiased contrastive learning for sequential recommendation
Current sequential recommender systems are proposed to tackle the dynamic user
preference learning with various neural techniques, such as Transformer and Graph Neural …
preference learning with various neural techniques, such as Transformer and Graph Neural …
Lightgt: A light graph transformer for multimedia recommendation
Multimedia recommendation methods aim to discover the user preference on the multi-
modal information to enhance the collaborative filtering (CF) based recommender system …
modal information to enhance the collaborative filtering (CF) based recommender system …
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 …
Linrec: Linear attention mechanism for long-term sequential recommender systems
Transformer models have achieved remarkable success in sequential recommender
systems (SRSs). However, computing the attention matrix in traditional dot-product attention …
systems (SRSs). However, computing the attention matrix in traditional dot-product attention …
The Research on Intelligent News Advertisement Recommendation Algorithm Based on Prompt Learning in End-to-End Large Language Model Architecture
With the explosive growth of information on the internet, users are increasingly facing the
problem of information overload, making precise news and ad recommendations an …
problem of information overload, making precise news and ad recommendations an …
Strategy-aware bundle recommender system
A bundle is a group of items that provides improved services to users and increased profits
for sellers. However, locating the desired bundles that match the users' tastes still …
for sellers. However, locating the desired bundles that match the users' tastes still …
Enhancing sequential recommendation with contrastive generative adversarial network
Sequential recommendation models a user's historical sequence to predict future items.
Existing studies utilize deep learning methods and contrastive learning for data …
Existing studies utilize deep learning methods and contrastive learning for data …
Personalized behavior-aware transformer for multi-behavior sequential recommendation
Sequential Recommendation (SR) captures users' dynamic preferences by modeling how
users transit among items. However, SR models that utilize only single type of behavior …
users transit among items. However, SR models that utilize only single type of behavior …
A generic behavior-aware data augmentation framework for sequential recommendation
Multi-behavior sequential recommendation (MBSR), which models multi-behavior
sequentiality and heterogeneity to better learn users' multifaceted intentions has achieved …
sequentiality and heterogeneity to better learn users' multifaceted intentions has achieved …
Multi-behavior generative recommendation
The task of multi-behavioral sequential recommendation (MBSR) has grown in importance
in personalized recommender systems, aiming to incorporate behavior types of interactions …
in personalized recommender systems, aiming to incorporate behavior types of interactions …