Social-Aware Multimodal Graph Learning for Micro-Video Popularity Prediction

Micro-videos, characterized by their brief duration and rich multi-modal content, have become increasingly popular on digital platforms. Micro-Video Popularity Prediction (MVPP) is crucial for understanding user behavior, identifying emerging trends, and leveraging commercial opportunities. While existing MVPP methods predominantly focus on extracting features from the multi-modal content of micro-videos, they often overlook the valuable social interactions that occur within micro-video platforms. In this paper, we introduce a novel Social-Aware Multimodal Graph Learning (SAMGL) approach that integrates multi-modal content and social information to enhance MVPP. First, we propose Social-Aware Multimodal Precomputation (SAMP) to efficiently utilize complete graph information in micro-video popularity prediction, aggregating multi-modal signals to mitigate information loss. Second, we introduce Retrieval Augmentation Prediction (RAP), which retrieves similar micro-videos to enhance prediction by incorporating global content features beyond direct graph connections. To facilitate our approach, we construct the datasets by incorporating social interactions as graph information. Extensive experiments on the datasets demonstrate that our model achieves state-of-the-art performance, validating the effectiveness of integrating social information and the proposed local-global aggregation mechanisms.