## 1 Introduction  
In the age of big data, enormous amounts of data from various sources are generated within milliseconds, inundating the information landscape and leading to overwhelming challenges. Recommendation systems emerged long ago as a solution to alleviate this information overload, offering users curated lists of preferred items and helping businesses to optimize user experiences [1]. While many users may benefit from these recommendation models by effortlessly discovering items of interest, businesses generate revenue by showcasing the products that appeal to their customers. Existing traditional recommendation systems include methods like collaborative filtering (CF), content-based filtering (CBF), and hybrid approaches (CF and CBF combination) [2]. These have been known for a long time as popular recommendation techniques. Although these methods assist many people, researchers continue to seek to enhance their effectiveness and to address real-world challenges. Furthermore, traditional methods, like collaborative filtering, often suffer from existing issues, like \"sparsity\" and \"new user\" problems, particularly the new-user problem as a cold-start issue. While it is relatively easy to search for online items randomly, identifying the most suitable ones for users remains a complex endeavor. Simultaneously, deep learning has emerged as a powerful paradigm for navigating the complicacy of data, making it particularly well suited in the era of big data for modern recommendation algorithms. Deep learning, often likened to a \"black box\", delves deeply into data through hidden layers and subsequently generates outputs. A variety of domains have harnessed deep-learning models to construct highly developed systems. [PERSON], [PERSON], [PERSON], and [PERSON] [3] employed encoder technology to design a recommendation system. Beyond deep learning, reinforcement learning has garnered significant attention from scholars. This progressive approach incorporates essential elements, such as states, actions, environments, and agents, providing a dynamic framework for solving complex problems [4]. Both deep learning and reinforcement learning have become prominent as influential subfields within the extensive domain of artificial intelligence. The use of deep learning is most useful for problems with high-dimensional-state space [5], while reinforcement learning can solve problems of more complicated tasks with lower prior knowledge because of its ability to learn different levels of abstractions from the data [6]. Despite their differing core principles and applications, these two fields share common factors that have contributed to their widespread adoption and recognition in recent years. Furthermore, these innovative methodologies have played pivotal roles in the realm of recommendation systems. This study offers significant advancements in movie recommendation systems by effectively combining the strengths of DRL and CF, addressing key challenges such as personalization, scalability, and the cold-start problem, with the aim of creating a more robust recommendation system. The main contribution arising from our research works encompasses the following:  
* The use of singular value decomposition (SVD) is utilized for matrix factorization in CF, extracting informative embeddings that capture latent user and movie features, which are essential for improving recommendation accuracy and personalization. * The work emphasizes the actor-critic method within DRL, a strategy that balances policy-based and value-based methods, potentially enhancing the recommendation process. The incorporation of the Deep Deterministic Policy Gradient (DDPG) into the reinforcement learning framework facilitates the training of the recommendation system through continuous interactions between users and the environment, ensuring a flexible and adaptable approach to Top-N recommendation mechanisms. The system updates its internal state to reflect the most recent user interactions, ensuring an up-to-date set of suggestions.