Description: Relevance feedback in multimodal systems is an approach that integrates user opinions and interactions to optimize the performance of information retrieval systems that handle multiple data modalities, such as text, images, audio, and video. This method allows systems to learn and adapt to the specific preferences and needs of users, thereby improving the accuracy and relevance of the results presented. By incorporating user feedback, these systems can adjust their search and ranking algorithms, resulting in a more personalized and effective experience. Feedback can be explicit, where users provide ratings or direct comments, or implicit, where it is inferred from user behavior, such as clicks or viewing time. This approach is particularly relevant in the current context, where the amount of available information is overwhelming, and the ability to filter and present relevant content becomes a critical challenge. Relevance feedback not only enhances user interaction but also contributes to the development of smarter and more adaptive systems that can evolve over time, reflecting changes in user preferences and behaviors.
