EWMIGCN: Emotional Weighting Based Multimodal Interaction Graph Convolutional Networks for Personalized Prediction

To address the challenges of information overload and cold start in personalized prediction systems, researchers have proposed graph neural network-based recommendation methods. However, existing studies have largely overlooked the shared or similar characteristics among different modal features. Moreover, there is a mismatch between the focuses of multimodal feature extraction (MFE) and user preference modeling (UPM). To tackle these issues, this paper establishes an interaction graph by extracting multimodal information and addresses the mismatch between MFE and UPM by constructing an emotion-weighted bisymmetric linear graph convolutional network (EW-BGCN). Specifically, this paper introduces a novel model called EWMIGCN, which combines multimodal information extraction using parallel CNNs to build an interaction graph, propagates the information on EW-BGCN, and predicts user preferences by summing the expressions of users and items through inner product calculations. Notably, this paper incorporates sentiment information from user comments to finely weigh the neighborhood aggregation in EW-BGCN, enhancing the overall quality of items. Experimental results demonstrate that the proposed model achieves superior performance compared to other baseline models on three datasets, as measured by HitsRatio with Normalized Discounted Cumulative Gain.