معرفی
Oleg Lashinin is an active researcher in the field of Recommender Systems, with a focus on Machine Learning, Temporal Modeling, and User Behavior Analysis. He has contributed to 15 recent publications spanning 2021–2025, including conference papers at ECIR, SIGIR, RecSys, and workshops like KaRS@RecSys and ORSUM@RecSys. His work explores advanced techniques such as Self-Attention Models, Time-Aware Item Weighting, and Cost-Constrained Recommendations.
Key research trends in his publications include Deep Learning for sequential recommendation tasks, Crowdsourcing for explanation evaluation, and Temporal Dynamics in user behavior. Notable projects include the GPT3RecBot Telegram chatbot and the RecBaselines2023 dataset for benchmarking recommender systems.
