Wimi Developed A Machine Learning-based Multi-modal Fusion Recommendation System

fusion: The next step is to fuse the multi-modal data collected from different sources. This step is to combine the data from different modalities into a single representation. The fusion of multi-modal data can be achieved by various methods such as concatenation, feature-level fusion, and decision-level fusion.

Model training: After the multi-modal data is fused, the next step is to train a model to learn the user’s preferences from the fused data. This step is to build a model that can accurately predict the user’s preferences from the fused data. Various machine learning algorithms such as deep learning, support vector machines, and decision trees can be used to train the model.