From Viewer Preferences to Personalized Experiences

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

The authors conducted a bibliometric analysis of 765 papers on movie and television recommendation systems published between 2015 and 2025. The goal was to systematically chart the field's evolution from precise recommendations to personalized services while exposing key gaps in measuring diversity, fairness, and interpretability. They evaluated relevant literature from the Web of Science Core Collection and Scopus databases, using Bibliometrix and VOSviewer tools to map publication trends, global coauthorship clusters, and co-citation networks. The analysis revealed a 21.54% annual publication growth rate, indicating a distinct methodological shift from traditional collaborative filtering toward dynamic deep learning models and multimodal content fusion. These findings mean that modern systems are evolving from pure matching algorithms into human-centric service ecosystems, which requires urgent advances in real-time continuous feedback loops, cross-modal semantic understanding, and robust privacy protection.