معرفی
Aleksandr Petrov is a Tutor in the School of Computing Science at the University of Glasgow. His research focuses on recommendation systems, machine learning, and information retrieval, with a particular emphasis on large-scale and sequential recommendation challenges. Recent work includes developing efficient methods for handling millions of items in recommendation systems, improving fairness and accuracy in sequential models, and leveraging generative AI for semantic search integration.
Publications highlight advancements in dynamic pruning techniques for sub-item embeddings, fairness in group-based recommendations, and optimizing transformer models for low-latency inference. His contributions span both algorithmic innovation and practical system design, addressing scalability and real-world deployment constraints in AI-driven recommendation engines.
No scientific awards or grants are explicitly listed in the provided information. Mr. Petrov advises no students in the current dataset, though his research often involves collaborative projects with peers like C. Macdonald and N. Tonellotto.
