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 …
A survey on trustworthy recommender systems
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …
deployed in almost every corner of the web and facilitate the human decision-making …
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 …
Cross-view hypergraph contrastive learning for attribute-aware recommendation
Recommender systems typically model user–item interaction data to learn user interests and
preferences. However, user interactions are often sparse and noisy. Moreover, existing …
preferences. However, user interactions are often sparse and noisy. Moreover, existing …
T&TRS: robust collaborative filtering recommender systems against attacks
F Rezaimehr, C Dadkhah - Multimedia Tools and Applications, 2024 - Springer
In recent years, the Internet has had a main and important contribution to human life and the
amount of data on the World Wide Web such as books, movies, videos and, etc. increase …
amount of data on the World Wide Web such as books, movies, videos and, etc. increase …
Bias assessment approaches for addressing user-centered fairness in GNN-based recommender systems
In today's technology-driven society, many decisions are made based on the results
provided by machine learning algorithms. It is widely known that the models generated by …
provided by machine learning algorithms. It is widely known that the models generated by …
Investigating the robustness of sequential recommender systems against training data perturbations: an empirical study
Sequential Recommender Systems (SRSs) have been widely used to model user behavior
over time, but their robustness in the face of perturbations to training data is a critical issue …
over time, but their robustness in the face of perturbations to training data is a critical issue …
Group validation in recommender systems: Framework for multi-layer performance evaluation
Evaluation of recommendation systems continues evolving, especially in recent years. There
have been several attempts to standardize the assessment processes and propose …
have been several attempts to standardize the assessment processes and propose …
Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training
Sequential recommendation models, models that learn from chronological user-item
interactions, outperform traditional recommendation models in many settings. Despite the …
interactions, outperform traditional recommendation models in many settings. Despite the …
Boosting Meta-Learning Cold-Start Recommendation with Graph Neural Network
Meta-learning methods have shown to be effective in dealing with cold-start
recommendation. However, most previous methods rely on an ideal assumption that there …
recommendation. However, most previous methods rely on an ideal assumption that there …