###### Abstract  
Traditional movie recommendation systems are increasingly falling short in the contemporary landscape of abundant information and evolving user behaviors. This study introduced the temporal knowledge graph recommender system (TKGRS), a ground-breaking algorithm that addresses the limitations of existing models. TKGRS uniquely integrates graph convolutional networks (GCNs), matrix factorization, and temporal decay factors to offer a robust and dynamic recommendation mechanism. The algorithm's architecture comprises an initial embedding layer for identifying the user and item, followed by a GCN layer for a nuanced understanding of the relationships and fully connected layers for prediction. A temporal decay factor is also used to give weightage to recent user-item interactions. Empirical validation using the MovieLens 100K, 1M, and Douban datasets showed that TKGRS outperformed the state-of-the-art models according to the evaluation metrics, i.e., RMSE and MAE. This innovative approach sets a new standard in movie recommendation systems and opens avenues for future research in advanced graph algorithms and machine learning techniques. recommendation systems; matrix factorization; temporal dynamics; graph convolutional networks +
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fill this gap by introducing a novel algorithm that addresses user-item interactions' static and dynamic aspects. As underscored by recent studies [7; 8], the static nature of these advanced models hampers their ability to adapt to the dynamic shifts in users' preferences. These shifts can be influenced by real-time events, seasonal changes, or trending topics, making the integration of temporal dynamics into recommendation algorithms an imperative rather than a luxury. In addressing the identified gaps in the existing literature, this study introduced the temporal knowledge graph recommender system (TKGRS), an innovative algorithm that synergistically integrates matrix factorization, graph convolutional networks (GCNs), and temporal dynamics. TKGRS aims to overcome the limitations of the current algorithms while establishing a new benchmark in personalized recommendation systems [7; 9]. Initially, TKGRS uses matrix factorization techniques to create initial embeddings for users and items, capturing their linear relationships. These embeddings serve as the foundation upon which GCNs act to update them, adding a layer of complexity by considering non-linear relationships and semantic connections in the user-item graph. To bring in the temporal aspect, a time decay factor is applied to user-item interactions, giving more weight to recent interactions while diminishing the impact of older ones. This multi-layered approach enables TKGRS to understand both the static and dynamic aspects of user-item interactions. The final recommendations are generated through a sequence of fully connected layers that map these updated embeddings to output the predictions. The algorithm's mathematical and computational foundations were explored rigorously. We validated its effectiveness using empirical evaluations, particularly focusing on the MovieLens 100K, 1M, and Douban datasets, and showed the best performance based on the evaluation metrics, i.e., RMSE and MAE. In addition, we used the \(t\)-test to show the significant difference between TKGRS and the best benchmark to justify the proposed algorithm. By harmonizing diverse technological advancements in matrix factorization, GCNs, and temporal modeling, TKGRS emerged as a versatile, robust solution for modern recommendation scenarios. Whether it captures the foundational user-item relationships, enriches these through semantic understanding via knowledge graphs, or adapts to real-time changes in users' behavior, TKGRS provides a holistic perspective to the complex challenges faced by current movie recommendation systems. The remainder of this study is organized as follows.