Abstract:

Abstract Recommender systems play a vital role in providing users with personalized information and enhancing their browsing experiences. However, despite the advancements in collaborative filtering techniques, several challenges persist in movie recommendation systems, including the cold start problem, scalability limitations, and data sparsity. The cold start problem arises when there is insufficient data to establish connections between users and items, resulting in inaccurate recommendations. Data sparsity further complicates the issue by making it difficult to identify reliable similar users due to the limited ratings provided by active users. Scalability poses yet another challenge, as real-time environments with a high number of users and extensive data processing requirements struggle to deliver efficient recommendations. To address these issues, this paper proposes a semantic approach that leverages singular value decomposition (SVD), a matrix factorization technique. By applying SVD, the system reduces the dimensionality of the data, overcoming the limitations of the cold start problem, scalability, and data sparsity. Experimental results demonstrate the effectiveness of the proposed system, showcasing improved recommendation accuracy and the ability to generate reliable suggestions even in situations with limited data. Moreover, the system showcases scalability by efficiently processing large volumes of data in real-time, ensuring seamless user experiences. Overall, this semantic approach offers a comprehensive solution to tackle the challenges of scalability, data sparsity, and the cold start problem in movie recommendation systems, potentially enhancing user satisfaction and recommendation quality.

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