Predicting Movie Hits Before They Happen with LLMs
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910928284418048 |
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| author | Agah, Shaghayegh Kim, Yejin Sharma, Neeraj Nankani, Mayur Foley, Kevin Huang, H. Howie Hamidian, Sardar |
| author_facet | Agah, Shaghayegh Kim, Yejin Sharma, Neeraj Nankani, Mayur Foley, Kevin Huang, H. Howie Hamidian, Sardar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02693 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Movie Hits Before They Happen with LLMs Agah, Shaghayegh Kim, Yejin Sharma, Neeraj Nankani, Mayur Foley, Kevin Huang, H. Howie Hamidian, Sardar Information Retrieval Computation and Language 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. |
| title | Predicting Movie Hits Before They Happen with LLMs |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2505.02693 |