Scholar
Milad Sabouri
Google Scholar ID: BJXsuUEAAAAJ
DePaul University
Machine Learning
Reinforcement Learning
Recommender Systems
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Citations & Impact
All-time
Citations
54
H-index
3
i10-index
2
Publications
8
Co-authors
9
list available
Contact
Email
msabouri@depaul.edu
GitHub
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Publications
4 items
Effectiveness of LLMs in Temporal User Profiling for Recommendation
2025
Cited
0
Using LLMs to Capture Users' Temporal Context for Recommendation
2025
Cited
0
Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendations
2025
Cited
0
Towards Explainable Temporal User Profiling with LLMs
2025
Cited
0
Resume (English only)
Background
Applied Scientist and Ph.D. candidate in Computer Science at DePaul University
Focuses on building personalized and intelligent decision systems
Brings extensive industry experience as a software engineer, emphasizing robust, scalable, and maintainable models
Current research centers on LLM-powered AI agents for dynamic and explainable user modeling in recommender systems
Leverages reinforcement learning and deep learning to enhance personalization while ensuring model transparency
Co-authors
9 total
Bamshad Mobasher
School of Computing, DePaul University
Masoud Mansoury
Assistant Professor, Delft University of Technology
Kun Lin
DePaul University
Yong Zheng
Associate Professor, Illinois Institute of Technology, USA
Co-author 5
Himan Abdollahpouri
Senior Research Scientist at Spotify
Robin Burke
University of Colorado, Boulder
Mykola Pechenizkiy
Eindhoven University of Technology
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