2026-06-07 –, Grand Hall 1
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.
Recommendation systems are a core component of many data-driven products, yet most practitioners are still navigating how and when to incorporate Large Language Models into these systems effectively.
This talk presents a practical, end-to-end view of LLM-based recommendation systems. We start by revisiting classical recommendation architectures and then move into modern approaches built around embeddings, vector similarity search, and retrieval-augmented generation (RAG).
Topics covered include:
Using LLM embeddings for user and item representation
Hybrid retrieval pipelines combining vector search and traditional ranking models
Prompt-driven personalization and context-aware recommendations
Offline and online evaluation strategies for LLM-based recommenders
Trade-offs around latency, cost, and system complexity
The focus is on real-world applicability rather than theoretical novelty. Examples and design patterns are drawn from production-like systems and practical experimentation. This session is aimed at data scientists, ML engineers, and practitioners who want to move beyond hype and build recommendation systems that deliver meaningful personalization using LLMs.
Hello world! 👋
I'm Özge Çinko. I'm currently an AI Engineer at ING, working around agentic AI. Before that, I spent two years as an AI Research Engineer at Huawei, where I focused on research-driven AI systems, including recommender systems. I hold a Bachelor's degree in Computer Engineering from Sakarya University. For me, engineering is a creative craft: a way of turning thoughts, emotions, and curiosity into experiences. I care about building technology that feels more purposeful, more human, and more alive. I love researching, building, learning, and exploring because they make me feel alive in the deepest way. I also love expressing myself through writing, speaking, and meaningful conversations, often inspired by art along the way.