The rapid growth of film, video, and multimedia content on new media platforms has intensified information overload, increasing the importance of effective recommender systems. Traditional recommendation approaches face limitations in modeling complex content semantics and dynamic user preferences. Deep learning techniques have been widely adopted to enhance film and multimedia recommendation performance. This study presents a bibliometric mapping and systematic literature review of deep learning film and multimedia recommendation systems in new media. Scopus was used as the primary data source, yielding 679 peer-reviewed studies following a structured screening and inclusion process. The research methodology, search strategy, and selection criteria are explicitly documented. Bibliometric techniques, including citation analysis, keyword co-occurrence, and thematic clustering, are applied to identify influential publications, dominant research streams, and emerging trends. The reviewed literature is synthesized into major thematic areas, including multimodal representation learning, graph-based recommendation, multimedia feature extraction, personalization and cold-start mitigation, fairness and bias, emotion-aware recommendation, and explainability. The findings reveal a strong dominance of multimodal and graph-based deep learning models, particularly those integrating visual, audio, textual, and interaction data. However, many existing approaches rely on shallow feature fusion and demonstrate limited capability in capturing fine-grained semantic relationships, user attraction mechanisms, and contextual meaning. Challenges related to cold-start, sparse feedback, fairness, transparency, and user experience remain insufficiently addressed. This study identifies critical research gaps and outlines future research directions, emphasizing the need for semantically rich, explainable, fair, and human-centered multimedia recommender systems capable of supporting the evolving complexity of new media ecosystems.
Linlin Hou (Thu,) studied this question.