Review of Multi-Modal Recommendation Systems: Integrating Video, Image and Text-based Techniques for Enhanced Personalization and Precision

At a time characterized by rapid growth of digital content and spreading systems of recommendations, the demand for personalized and accurate recommendations of unprecedented heights reached. Convergence more modalities such as video, pictures and text require the development of advanced algorithms and technologies to fulfill this challenge. This overview of literature examines and synthesizes the latest advances in the recommendations systems focusing on three different but interconnected domains. It immerses in the empire for multiple periods, increases the accuracy of image recommendations and the development of sequential and time systems of text -based recommendations. With the primary goal of detecting innovative techniques and technologies that are the basis of these areas, our review is aimed at providing a detailed view of the current state of research and development in the field of recommendations. To achieve this, we examine the integration of our own hybrid algorithms, multimodal attention mechanisms, advanced LSTM and GRU models, and text strategies that strengthen sequential, consciousness, periods and personalized items. Our inspection seeks to merge the knowledge of these diverse subfields and seeks to offer a holistic view of how advanced technologies transform recommendations systems. This synthesis can potentially serve as a valuable source for research workers, experts and creators of decisions who are determined by the continuing development of recommendations and technology of personalization.