Predicting Movie Hits Before They Happen with LLMs

TL;DR

This paper explores using Large Language Models to predict the future popularity of new movies based on metadata, aiming to improve cold-start recommendations and editorial fairness.

Contribution

It introduces a novel approach leveraging LLMs for cold-start movie popularity prediction, validated against baseline methods.

Findings

LLMs outperform traditional baselines in predicting movie popularity

The approach can be integrated into recommendation systems and editorial tools

Effective in identifying potentially overlooked movies

Abstract

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.