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In recent years, the proliferation of smartphones and advancements in internet technology have led to the emergence of various internet applications, resulting in a surge in data volume. However, this expansion has also brought about information overload. To address this issue, recommendation systems have gained prominence, leveraging recommendation algorithms to extract user-relevant items from extensive datasets based on user interests. These systems have found applications in various domains such as e-commerce and social networks. However, most collaborative filtering algorithms are tailored for sparse user-item rating matrices, neglecting scenarios with densely rated popular items by heavy users. This paper presents an improved collaborative filtering algorithm for dense data scenarios. It utilizes the K-Nearest Neighbors (KNN) approach, focusing on user rating predictions to tackle challenges arising from dense data environments. The proposed algorithm divides user ratings into liked and disliked item sets, enhancing prediction accuracy by calculating similarities separately. Experimental evaluations are performed using a real-world dataset from the "Kuaishou" video sharing app, demonstrating the algorithm's superiority in terms of F-Score compared to traditional collaborative filtering methods. The algorithm's simplicity, scalability, and performance are highlighted, showcasing its effectiveness in addressing recommendation challenges in real-world settings. Future research directions could explore its applicability across diverse datasets and domains.
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