The TUMP Lab advances trustworthy user modeling and personalization through cutting-edge AI techniques, human-centric LLM-based user simulation, and rigorous benchmarking via open-source frameworks.

Research Focus:

• Designing trustworthy recommendation algorithms—robust, diverse, fair, explainable, and privacy-preserving—by applying cutting-edge techniques e.g., deep learning and large language models across various domains including e-commerce, multimedia, location-based social networks, and healthcare.
• Developing LLM-based user simulators for human-centric evaluation and optimization of recommender systems.
• Benchmarking recommender systems through rigorous evaluation and fair comparison by advancing open-source libraries such as DaisyRec and DaisyRec-v2.0.