Predicting Movie Hits Before They Happen with LLMs
It addresses the cold-start issue for movies on entertainment platforms, which is incremental as it applies LLMs to a known bottleneck.
The paper tackles the cold-start problem for movie recommendation by forecasting popularity using Large Language Models (LLMs) on metadata, showing effectiveness compared to established baselines.
Addressing the cold-start issue in content recommendation remains a critical ongoing challenge. In this work, we focus on tackling the cold-start problem for movies on a large entertainment platform. Our primary goal is to forecast the popularity of cold-start movies using Large Language Models (LLMs) leveraging movie metadata. This method could be integrated into retrieval systems within the personalization pipeline or could be adopted as a tool for editorial teams to ensure fair promotion of potentially overlooked movies that may be missed by traditional or algorithmic solutions. Our study validates the effectiveness of this approach compared to established baselines and those we developed.