Large language models for generative recommendation: A survey and visionary discussions
Recent years have witnessed the wide adoption of large language models (LLM) in different
fields, especially natural language processing and computer vision. Such a trend can also …
fields, especially natural language processing and computer vision. Such a trend can also …
Graph neural networks for vulnerability detection: A counterfactual explanation
Vulnerability detection is crucial for ensuring the security and reliability of software systems.
Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding …
Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding …
Counterfactual explanation for fairness in recommendation
Fairness-aware recommendation alleviates discrimination issues to build trustworthy
recommendation systems. Explaining the causes of unfair recommendations is critical, as it …
recommendation systems. Explaining the causes of unfair recommendations is critical, as it …
Concept--An Evaluation Protocol on Conversation Recommender Systems with System-and User-centric Factors
The conversational recommendation system (CRS) has been criticized regarding its user
experience in real-world scenarios, despite recent significant progress achieved in …
experience in real-world scenarios, despite recent significant progress achieved in …
Understanding users' AI manipulation intention: An empirical investigation of the antecedents in the context of AI recommendation algorithms
T Kim, I Im - Information & Management, 2025 - Elsevier
This study examines antecedents that drive platform users to manipulate artificial
intelligence (AI) recommendation algorithms. Based on the persuasion knowledge model …
intelligence (AI) recommendation algorithms. Based on the persuasion knowledge model …
Causal Learning for Trustworthy Recommender Systems: A Survey
Recommender Systems (RS) have significantly advanced online content discovery and
personalized decision-making. However, emerging vulnerabilities in RS have catalyzed a …
personalized decision-making. However, emerging vulnerabilities in RS have catalyzed a …
[HTML][HTML] Neural Causal Graph Collaborative Filtering
Graph collaborative filtering (GCF) has emerged as a prominent method in recommendation
systems, leveraging the power of graph learning to enhance traditional collaborative filtering …
systems, leveraging the power of graph learning to enhance traditional collaborative filtering …
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum
Efficiently capturing consistent and complementary semantic features in a multimodal
conversation context is crucial for Multimodal Emotion Recognition in Conversation (MERC) …
conversation context is crucial for Multimodal Emotion Recognition in Conversation (MERC) …