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
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Publications
7 items
Routing Between Generative and Collaborative User Profiles: A Serving-Time Gate for Controllable Novelty
2026
Cited
0
When LLM-Inferred User Context Adds Value in Production Streaming Recommendation
2026
Cited
0
When LLM-Based User Profiling Adds Value in Production Streaming Recommendation
2026
Cited
0
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
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
7 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
Himan Abdollahpouri
Senior Research Scientist at Spotify
Robin Burke
University of Colorado, Boulder
Mykola Pechenizkiy
Eindhoven University of Technology