Optimizing Short Video Recommendation: A Multimodal Fusion Approach Leveraging Graph Neural Networks

Abstract In the dynamic landscape of contemporary social media, short videos have emerged as a dominant form of content consumption, prompting intensified research focus on short video recommendation scenarios. Users engaged with short videos exhibit unique characteristics, marked by diverse and multi-level dynamic interests. Addressing the challenges inherent in short video recommendation systems, this paper introduces a hybrid recommendation algorithm model that capitalizes on multimodal information. The model incorporates user-side auxiliary information into its network structure, delving into the profound interests of users. It assesses the significance of each dimension within user and item feature representations during the scoring prediction task. Furthermore, the application of graph neural networks in the recommendation system is enhanced through the integration of an attention mechanism. This mechanism facilitates the fusion of multi-layer state output information, enabling more effective participation of shallow structural features provided by the intermediate layer in the prediction task. Through extensive experimentation on different datasets, the proposed model demonstrates improved recommendation accuracy compared to traditional recommendation algorithms, affirming the feasibility and effectiveness of the approach.