Dr. Yashar Deldjoo is a senior research scientist and a tenure-track Assistant Professor
in Computer Science at the Polytechnic University of Bari, Italy.
His work focuses on integrating elements of trustworthy and responsible AI
(e.g., fairness, adversarial robustness, security) into recommender systems.
His current research is highly concentrated on harnessing the potential of Generative AI (e.g., large language models
and LLM agents) for recommender systems. He has contributed novel research on understanding and mitigating risks inherent in generative outputs—such as hallucinations,
randomness, and bias amplification. Notably, his work on evaluation frameworks for recommender systems first identifies these emerging
risks and
then proposes holistic evaluation strategies to ensure recommendations are not only accurate but also factually grounded
and ethically aligned.
He recently led a multidisciplinary collaboration involving Amazon, DeepMind, and several leading universities
for an upcoming book at FN&TIR'25,
Next-Generation Recommender Systems under Generative AI (Gen-RecSys),
which explores advanced generative techniques. A widely recognized survey version of this book was presented
at KDD'24.
He also discussed next-generation Gen-RecSys on the
Recsperts Podcast.
Another line of his research concerns multimodal learning for RecSys, Fashion AI, and
the music industry.
He serves as Associate Editor for IEEE TKDE and ACM CSUR, Distinguished Reviewer for ACM TORS,
and Guest Editor for special issues on Generative RecSys (2025) and Trustworthy RecSys (2024).
He is a Senior Program Committee (SPC) member at SIGIR, CIKM, ECAI, TheWebConf, and ACM RecSys.
He was recognized among the
top 2% of researchers worldwide (Stanford’s Ioannidis database, 2023–2024).
I investigate reliable and trustworthy AI in Recommender Systems, emphasizing:
My aim is to bridge theoretical models and real-world deployment, ensuring AI is socially responsible.
I'm interested in collaborating with students/post-docs in:
Feel free to reach out if you are passionate about pushing AI innovation responsibly.
Below is an extended selection of influential works (journals, conference proceedings, surveys, and book chapters). For a comprehensive list, please see Google Scholar or DBLP .
Below is an extended selection of influential works (journals, conference proceedings, surveys, and book chapters). For a comprehensive list, please see Google Scholar or DBLP.