Recommender systems in the era of large language models (llms)
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys)
have become an important component of our daily life, providing personalized suggestions …
have become an important component of our daily life, providing personalized suggestions …
Recommender systems in the era of large language models (llms)
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys)
have become an indispensable and important component in our daily lives, providing …
have become an indispensable and important component in our daily lives, providing …
BLoG: Bootstrapped graph representation learning with local and global regularization for recommendation
With the explosive growth of online information, the significant application value of
recommender systems has received considerable attention. Since user–item interactions …
recommender systems has received considerable attention. Since user–item interactions …
A survey of graph neural networks for social recommender systems
Social recommender systems (SocialRS) simultaneously leverage the user-to-item
interactions as well as the user-to-user social relations for the task of generating item …
interactions as well as the user-to-user social relations for the task of generating item …
Multi-intention oriented contrastive learning for sequential recommendation
Sequential recommendation aims to capture users' dynamic preferences, in which data
sparsity is a key problem. Most contrastive learning models leverage data augmentation to …
sparsity is a key problem. Most contrastive learning models leverage data augmentation to …
Fairly adaptive negative sampling for recommendations
Pairwise learning strategies are prevalent for optimizing recommendation models on implicit
feedback data, which usually learns user preference by discriminating between positive (ie …
feedback data, which usually learns user preference by discriminating between positive (ie …
Adversarial attacks for black-box recommender systems via copying transferable cross-domain user profiles
As widely used in data-driven decision-making, recommender systems have been
recognized for their capabilities to provide users with personalized services in many user …
recognized for their capabilities to provide users with personalized services in many user …
Jointly attacking graph neural network and its explanations
Graph Neural Networks (GNNs) have boosted the performance for many graph-related
tasks. Despite the great success, recent studies have shown that GNNs are still vulnerable to …
tasks. Despite the great success, recent studies have shown that GNNs are still vulnerable to …
Graph machine learning in the era of large language models (llms)
Graphs play an important role in representing complex relationships in various domains like
social networks, knowledge graphs, and molecular discovery. With the advent of deep …
social networks, knowledge graphs, and molecular discovery. With the advent of deep …
Graph pre-training and prompt learning for recommendation
GNN-based recommenders have excelled in modeling intricate user-item interactions
through multi-hop message passing. However, existing methods often overlook the dynamic …
through multi-hop message passing. However, existing methods often overlook the dynamic …