Multimodal machine learning for catalogue metadata correction in online retail

The quality of catalogue metadata affects the success of the e-commerce platforms at every level in search accuracy, customer satisfaction, and many other. This review examines the implementation of multimodal machine learning (MML) in correcting catalogue metadata, with the emphasis being put on the combination of the input of textual, visual, and structured data. It describes the theoretical underpinnings, model frameworks, fusion policies, and benchmarking procedures that are being actively used in the research. As empirical evidence, it has proven that MML methods performed more soundly than unimodal baselines at accuracy, F1 scores, and tasks involving metadata imputation. Another essential struggle, namely modality misalignment, interpretability, and domain generalization, is also mentioned in the review. The directions of future work are addressed, which include multilingual support, explainable AI, knowledge graph integration, and active learning. The current paper serves as a multifaceted guide to inform researchers and practitioners who are interested in enhancing the accuracy of metadata in a very large-scale retail setting of a digital nature.