Temporal knowledge graph predicts music festival lineups years ahead
A Temporal Knowledge Graph for Music Festival Lineup Forecasting
Machine Learning
Summary
Predicting which artists will perform at future music festivals is tricky because it depends on many factors like genres, tours, and past shows. The authors created a large dataset covering decades of festival lineups and related artist information to help make better predictions. They treated the problem as forecasting connections between artists and festivals over time and tested several computer models to see how well each could predict future lineups. They also compared these models to large language models that have no prior training for this task. This dataset can help improve future prediction tools and study how artists and festivals are related over time.
What this means in practice
- •For event organizers: Predict future music festival lineups by analyzing past artist-festival relationships to improve planning of artist bookings.
- •For recommendation system developers: Enhance music recommendation engines by forecasting artist popularity and festival appearances from temporal knowledge graph data.
Authors
Julia Gastinger, Thilo Dieing, Christian Meilicke, Heiner Stuckenschmidt
Abstract
Music festival lineups emerge from complex relationships among artists, genres, releases, labels, and past performances, making the prediction of future lineups a natural fit for temporal knowledge graph (TKG) forecasting. In this work, we present a TKG covering 380 festivals over 55 years, comprising more than 90K festival performance quadruples along with information on festivals, artist tours, and artist metadata, and release it as a resource for TKG forecasting evaluation. We formalize festival lineup forecasting as temporal link prediction between artists and festivals at future timestamps. We evaluate six TKG forecasting models on this task, analyze their capabilities and limitations, and compare them against Large Language Models applied zero-shot. Our resource complements existing TKG benchmarks by grounding evaluation in a concrete, real-world application domain.