Predicting Movie Hits Before They Happen with LLMs
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 15 references that are in the map. First posted on arXiv 5 May 2025.
Builds on
10 of 15 references.
References · 10
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2024The Llama 3 Herd of Modelscited by 14,715
- 2016Wide & Deep Learning for Recommender Systemscited by 747
- 2023Large Language Models are Effective Text Rankers with Pairwise Ranking Promptingcited by 307
- 2022RankT5: Fine-Tuning T5 for Text Ranking with Ranking Lossescited by 115
- 2017Joint Deep Modeling of Users and Items Using Reviews for Recommendationcited by 84
- 2023A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Modelscited by 55
- 2019SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrievalcited by 48
- 2017DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrievalcited by 21
- 2019Joint Neural Collaborative Filtering for Recommender Systemscited by 11
Built on by
Not cited yet.