ECP-Bench: Benchmarking and Learning Entertainment Content Promotion with Foundation Models

Abstract:Content promotion spans a broad set of skills, from understanding content to forecasting its market reception. However, LLMs' ability to support such promotion decisions remains underexplored. Existing studies are often limited to a single task (e.g., popularity prediction) or a small set of tasks within a single domain (e.g., movies). As a result, there is a lack of understanding of LLMs' abilities in the full promotion process and how these abilities generalize across different tasks and domains. In this work, we introduce ECP-Bench, a benchmark containing 1.9M movie, game, and music items and 423,451 questions across 33 tasks in five content-promotion skill families. Our evaluation shows that frontier models achieve only 51.9\% overall accuracy and lose much of their advantage on post-cutoff content, with drops of up to 19.1 percentage points. In contrast, open-weight models fine-tuned on ECP-Bench achieve up to 60.3\%, remain substantially more stable across the knowledge cutoff, generalize to unseen content and tasks, and exhibit meaningful cross-domain generalization.

Information Retrieval (cs.IR); Machine Learning (cs.LG)

arXiv:2609.22150 [cs.IR]

(or arXiv:2609.22150v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2609.22150 Focus to learn more arXiv-issued DOI via DataCite

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