A comprehensive survey on trustworthy recommender systems
As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …
people make appropriate decisions in an effective and efficient way, by providing …
Causal inference for recommendation: Foundations, methods and applications
Recommender systems are important and powerful tools for various personalized services.
Traditionally, these systems use data mining and machine learning techniques to make …
Traditionally, these systems use data mining and machine learning techniques to make …
[HTML][HTML] A survey on fairness-aware recommender systems
As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …
life by providing personalized suggestions and facilitating people in decision-making, which …
LightFR: Lightweight federated recommendation with privacy-preserving matrix factorization
Federated recommender system (FRS), which enables many local devices to train a shared
model jointly without transmitting local raw data, has become a prevalent recommendation …
model jointly without transmitting local raw data, has become a prevalent recommendation …
[HTML][HTML] Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention
Recent advancements in recommender systems have focused on integrating knowledge
graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced …
graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced …
Attention calibration for transformer-based sequential recommendation
Transformer-based sequential recommendation (SR) has been booming in recent years,
with the self-attention mechanism as its key component. Self-attention has been widely …
with the self-attention mechanism as its key component. Self-attention has been widely …
Equivariant contrastive learning for sequential recommendation
Contrastive learning (CL) benefits the training of sequential recommendation models with
informative self-supervision signals. Existing solutions apply general sequential data …
informative self-supervision signals. Existing solutions apply general sequential data …
[HTML][HTML] Keyword-enhanced recommender system based on inductive graph matrix completion
Going beyond the user–item rating information, recent studies have utilized additional
information to improve the performance of recommender systems. Graph neural network …
information to improve the performance of recommender systems. Graph neural network …
Recommender systems in cybersecurity
L Ferreira, DC Silva, MU Itzazelaia - Knowledge and Information Systems, 2023 - Springer
With the growth of CyberTerrorism, enterprises worldwide have been struggling to stop
intruders from obtaining private data. Despite the efforts made by Cybersecurity experts, the …
intruders from obtaining private data. Despite the efforts made by Cybersecurity experts, the …
A comprehensive review of recommender systems: Transitioning from theory to practice
Recommender Systems (RS) play an integral role in enhancing user experiences by
providing personalized item suggestions. This survey reviews the progress in RS inclusively …
providing personalized item suggestions. This survey reviews the progress in RS inclusively …