The challenge of connecting consumers with entertainment content that aligns with their tastes represents one of the defining problems of the digital streaming era. This paper presents a multi-method recommendation framework that uses cast and crew metadata as the primary signal for measuring inter-film similarity. Four computational approaches are developed and contrasted: TF-IDF vectorization with cosine similarity, k-Nearest Neighbours (k-NN), a hybrid Decision Tree K-Means classifier, and standalone K-Means clustering. Drawing on a large-scale film credits dataset, the study demonstrates how distinct algorithmic paradigms can extract complementary dimensions of similarity from identical underlying data. Empirical analysis reveals meaningful trade-offs in precision, cluster coherence, and scalability across methods, ultimately suggesting that ensemble deployment outperforms any single approach. The findings carry direct implications for platform designers seeking to improve content discovery and user retention in on-demand entertainment environments...
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