用大模型预测冷启动电影热度，助力平台发现潜在爆款。
Predicting Movie Hits Before They Happen with LLMs
- 基于电影元数据，用大模型预测冷启动影片的未来热度。
- 相比传统基线和自研模型，预测效果显著更优。
- 适合内容推荐系统与编辑团队用于发掘被忽略的好片。
内容推荐中的冷启动问题仍是持续挑战。本文聚焦大型娱乐平台上冷启动电影的推荐难题，目标是利用大语言模型（LLMs）结合电影元数据，提前预测其未来受欢迎程度。该方法可集成至个性化推荐系统的检索环节，或作为编辑团队的辅助工具，帮助公平推广那些可能被传统算法遗漏的潜力作品。实验表明，该方法在性能上优于现有基线及自研模型。
原文摘要 · Abstract (English)
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.
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