3 papers
cs.IR2025
Vectorized Context-Aware Embeddings for GAT-Based Collaborative Filtering
Danial Ebrat, Sepideh Ahmadian, Luis Rueda
Recommender systems often struggle with data sparsity and cold-start scenarios, limiting their ability to provide accurate suggestions for new or infrequent users. This paper prese…
cs.IR2025
End-to-End Personalization: Unifying Recommender Systems with Large Language Models
Danial Ebrat, Tina Aminian, Sepideh Ahmadian +1
Recommender systems are essential for guiding users through the vast and diverse landscape of digital content by delivering personalized and relevant suggestions. However, improvin…
cs.IR2025
Lusifer: LLM-based User SImulated Feedback Environment for online Recommender systems
Danial Ebrat, Eli Paradalis, Luis Rueda
Reinforcement learning (RL) recommender systems often rely on static datasets that fail to capture the fluid, ever changing nature of user preferences in real-world scenarios. Mean…