Multidimensional diversity in video recommender systems: a holistic framework of literature gaps and future directions

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Video recommender systems shape content discovery, user engagement, and information access across digital platforms such as YouTube, Netflix, and TikTok. Although recent advances in deep learning, graph-based models, and multimodal recommendation have improved personalization, key challenges remain, including limited diversity metrics, weak geometric analysis of embedding spaces, inadequate multimodal datasets, and insufficient integration of fairness and transparency. This review synthesizes recent research on diversity-aware video recommender systems through a structured literature review. The screening process identified 572 records from six scholarly sources; after title–abstract screening and full-text eligibility assessment, 101 studies were included in the qualitative synthesis. The findings show that diversity is often treated as a narrow, one-dimensional objective, while broader system-level, user-centered, and societal perspectives remain underdeveloped. The review therefore argues for multidimensional and human-centered recommendation frameworks that incorporate geometric diversity modeling, multi-objective optimization, long-term user satisfaction, and user-controllable design. The study provides a conceptual basis for developing more robust, fair, transparent, and sustainable video recommender systems.