## 1 Introduction  
In the era of big data, the exponential growth of online information has made recommendation systems indispensable for mitigating information overload. These systems have been widely applied across domains such as e-commerce, social media, and content delivery platforms. Traditional collaborative filtering (CF) techniques, which rely on historical user-item interaction data to infer user preferences, often suffer from two major issues: data sparsity and the cold-start problem [1, 2, 3]. To address these challenges, Knowledge Graphs (KGs)--structured semantic networks encoding entities and their interrelations--have been introduced into recommendation frameworks. By enriching item representations with rich entity attributes and relation semantics, KGs enable more informed inference [4]. For instance, in the movie domain, a KG can link items like Inception and Interstellar via shared relations such as \"director\" or \"genre\", thereby capturing semantic similarity beyond user behavior alone. Current KG-based recommendation approaches can be grouped into three main categories: embedding-based methods, which learn vector representations of entities and relations path methods (e.g., TransE [5], CKE [6]); path-based methods, which mine semantic paths between users and items (e.g., RippleNet [7] and MCRec [8]); and graph neural network (GNN)-based methods [9], which leverage the power of graph structures to model user-item interactions. Methods such as KGAT [10] and KGINKGIN [11] have demonstrated the effectiveness of leveraging attention-based and relation-aware propagation mechanisms, respectively. Specifically, KGAT uses attention mechanisms to weight neighbors, and KGIN employs relation-aware propagation to separate collaborative and knowledge signals. These methods have improved recommendation performance by capturing multi-hop dependencies, but they still face two critical challenges: the sparsity of long-tail relations and the inability to effectively model dynamic user preferences. Despite their success, these approaches still exhibit significant limitations: First, long-tail relationships--which dominate most real-world KGs--often occur infrequently, making it difficult to learn quality embeddings for rare relations [12; 13]. Second, current models usually assume static user preferences, failing to capture users' evolving interests over time [14; 15]. Third, existing GNN models often treat relations independently and ignore higher-level semantic clusters formed by related relations (e.g., \"Director\" + \"Actor\" -> \"Creative Team\"). To overcome these issues, we propose a novel recommendation framework, GLARA, which integrates a Virtual Relational Knowledge Graph (VRKG) with a Graph Attention Network (GAT). The VRKG is constructed by clustering semantically similar relations (e.g., \"Director\" and \"Writer\") into virtual relation groups (e.g., \"Creative Team\") using unsupervised methods. This abstraction mechanism not only alleviates the data sparsity caused by long-tail relations but also enhances semantic connectivity, ensuring better generalization in scenarios with sparse data. Furthermore, GLARA introduces a two-level optimization framework that synergistically combines both global and local perspectives: At the global level, a Local Weighted Smoothing (LWS) module aggregates semantic information across related nodes, promoting embedding convergence for semantically similar entities. This process ensures that the embeddings remain consistent across the graph, mitigating the sparsity of long-tail relations. At the local level, a GAT layer dynamically assigns attention weights to recent interactions, enabling the model to capture fine-grained, temporally adaptive user preferences. The combination of VRKG's global semantic abstraction and GAT's local attention-based adaptation offers a novel synergy that is not achievable by either approach alone. This integrated framework allows GLARA to effectively address both the long-tail problem and the dynamic nature of user preferences.