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Top1. Introduction
Personalized recommendation systems have become a cornerstone of modern digital platforms(Silvester & Kurian, 2023), including e-commerce, social media, and streaming services, playing a pivotal role in enhancing user experience and boosting business performance(Amiri et al., 2024). By analyzing user behaviors, such as clicks, purchases, and browsing patterns, these systems aim to predict and recommend items that align with individual preferences(Cheng & Peng, 2024). However, effectively mining and utilizing the intricate, multi-modal user behavior data presents several challenges, especially in scenarios involving sparse interactions and dynamic user interests(X. Wang et al., 2024).
One of the primary challenges in integrating multi-modal data is the inherent heterogeneity among different data types(Yeh & Wang, 2023), such as textual reviews, visual product features, and behavioral interaction data. These modalities often exist in diverse formats and scales, making it difficult to align and fuse them effectively. Additionally, inconsistent or missing data across modalities can introduce noise and bias, compromising recommendation quality(Wang et al., 2025). The high computational complexity associated with processing multi-modal data further complicates real-time recommendation tasks, where fast and efficient predictions are critical. Existing models frequently struggle to balance the contributions of each modality, often over-relying on one type of data and neglecting others, which limits the diversity and accuracy of recommendations.
Recent years have witnessed significant advancements in recommendation algorithms, particularly those leveraging deep learning techniques(Li et al., 2025; Patil et al., 2024). These methods, including Neural Graph Collaborative Filtering (NGCF)(Kobiela et al., 2023) and self-supervised learning-based frameworks(Yu et al., 2023; Zhang et al., 2025), have demonstrated their potential in capturing collaborative filtering (CF)(Koren et al., 2021) signals through graph-based representations of user-item interactions. Despite these advancements, existing approaches often fall short in fully modeling high-order connectivity, dynamic user interests, and the multi-modal nature of data(Meng et al., 2023; Nguyen et al., 2023). These limitations hinder their ability to provide accurate and personalized recommendations, particularly in scenarios with limited data or rapidly changing user preferences(Jing & Qing, 2024; Zhang et al., 2021).
Addressing these challenges requires a holistic approach that integrates high-order collaborative signals, dynamic interest modeling, and multi-modal data fusion(Ping & Yue, 2024). The core difficulty lies in effectively capturing the high-order relationships in user-item interaction graphs while simultaneously addressing the temporal dynamics of user behaviors and the inherent heterogeneity of multi-modal data(Miao, 2023). Furthermore, ensuring scalability and robustness in such systems remains a pressing issue as data volume and complexity increase(Deekshith, 2023).
To tackle these challenges, this study proposes the Graph Attention-based Dynamic Recommendation Framework (GADR). This framework builds upon recent advances in graph neural networks (GNNs) and dynamic modeling techniques(Skarding et al., 2021). GADR uniquely addresses the complexities of multi-modal data integration by leveraging a graph attention mechanism (GAT) that dynamically assigns importance to different user-item interactions, ensuring that textual, visual, and behavioral data are effectively aligned and contribute meaningfully to the recommendation process(Cui et al., 2024). This dynamic attention mechanism helps mitigate noise and improves the robustness of multi-modal fusion(Li & Song, 2024), overcoming the limitations of traditional models(Quan et al., 2024).