Multi-modal Bayesian Recommendation System

In this work, we introduce a Multi-modal Bayesian Recommendation System (MBR) that leverages both image and text modalities to enhance recommendation quality using common implicit feedback. Utilizing deep convolutional neural networks for image feature extraction and a language representation model for textual analysis, our proposed method effectively integrates these modalities to understand their collective impact on user preferences. Experiments on a large-scale dataset demonstrate the efficacy of MBR in providing improved recommendations.