Addressing information bias in multimodal recommendation systems based on expert systems

The proliferation of recommendation systems has revolutionized information retrieval by helping users efficiently navigate through large data sets. However, these systems often suffer from information bias, especially in multimodal recommendation setups. This paper addresses the issue of bias mitigation in multimodal recommendation systems using expert systems. Through a comprehensive literature review, various techniques such as knowledge graph integration, multimodal fusion, and deep learning architectures are explored. Furthermore, a novel approach using dynamic expert meeting algorithms for bias mitigation is proposed. Theoretical frameworks of expert systems are discussed, highlighting their adaptive capabilities and applicability to diverse domains. Then, the methodology for addressing information bias in multimodal recommendation systems is presented, including experimental analysis and relevance tagging. The results demonstrate the effectiveness of the proposed approach in reducing bias and improving recommendation accuracy.