04-23, 14:10–14:40 (Europe/Berlin), B09
Discover how graph algorithms are transforming content recommendation in this insightful talk. We'll journey from the basics of graph-based models, exploring simple graph walks, to the cutting-edge realm of Graph Neural Networks. Uncover the power of graph embeddings and learn when graph-based approaches excel in recommender systems.
In this talk, we'll explore how the complex problem of content recommendation transforms when viewed through the innovative lens of graph algorithms.
Imagine a world where content and users form a bi-partite graph, and the key to unlocking personalized recommendations lies in predicting links and weights within this graph. We'll embark on a journey starting from the foundational graph-based recommender models, where simple graph walks lay the groundwork.
As we delve deeper, we'll uncover the potent capabilities of graph embeddings and the transformative impact of Graph Neural Networks.
Finally, we'll wrap up with valuable insights on the scenarios where graph-based approaches shine the brightest in solving recommender problems. Whether you're a seasoned data scientist or new to the field of machine learning, this talk will equip you with a fresh perspective on leveraging graphs for sophisticated and effective content recommendation strategies.
Intermediate
Expected audience expertise: Domain –Intermediate
Abstract as a tweet (X) or toot (Mastodon) –Content Recommendation with Graphs: From Basic Walks to Neural Networks
Mirza Klimenta received his PhD in Computer Science from the University of Konstanz (Germany) at age 25. While in academia, Mirza worked in the fields of dimension reduction and graph embedding, and his work has been recognized by the scientific community. As a (Senior) Data Scientist, Mirza focuses on Recommender Systems and Algorithm Engineering. His most notable work is in the design and implementation of a Recommender System powering ARD Audiothek, one of the most popular audio-on-demand platforms in Germany. He is also a writer of literary fiction.