A Sommelier’s Guide to Recommendation Algorithms: Classical and Graph-Based Recommender Systems

Session Track: Data Intelligence

Session Time: 12:30 pm - 01:00 pm November 6

Session description

Recommendation engines are all around us – on Netflix, Spotify, Amazon and many other platforms, they subtly shape what we watch, listen to, or buy. In fact, around 80% of what users watch on Netflix comes from recommendations, making these systems critical for user satisfaction and engagement. But building effective recommendation systems comes with challenges: massive datasets, complex user preferences, and the need for fast, accurate predictions. In this talk, Moritz will explore how to build a recommendation engine using the X-Wines dataset (https://github.com/rogerioxavier/X-Wines). He will start by presenting classical, non-graph-based approaches and then contrast them with a graph-based solution using Neo4j. The talk will walk through implementation details, key metrics for comparing different recommendation strategies, and a hands-on evaluation of both approaches. Finally, Moritz will highlight the strengths and limitations of each method and discuss when a graph-based system might provide a real advantage.