Özge Çinko
Hello World, I'm Özge Çinko! 👋
I'm a computer engineer who finds inspiration at the intersection of curiosity and technology. Currently building the future as an AI Engineer at ING.
For me, engineering is a creative craft - turning data into narratives and emotions into visual experiences. I am passionate about making technology more human-centric and purposeful.
When I'm not coding, I'm usually writing, traveling, or chasing the thrill of learning something new.
Session
Large Language Models are rapidly changing how we think about recommendation systems. Traditional pipelines based on collaborative filtering or matrix factorization are being complemented and sometimes replaced by embedding-based and LLM-driven approaches.
In this talk, we explore how modern recommendation systems can be built using LLM embeddings, vector databases, and hybrid architectures that combine classical ML with generative models. We will discuss practical design patterns for personalization, retrieval, ranking, and user modeling, focusing on real-world constraints such as latency, cost, and evaluation.
The session emphasizes hands-on insights from production systems and highlights where LLMs add real value and where they don’t. Attendees will leave with a clear mental model for designing scalable, LLM-powered recommendation systems beyond toy examples.