Understanding Recommender Algorithms: Exploring The Role Of Data Mining And User Behavior Analysis

Recommender algorithms play a vital role in various domains by providing users with personalized content suggestions. This article explores the intersection of data mining and user behavior analysis in enhancing recommender algorithms. By leveraging data mining techniques, such as clustering, classification, and pattern recognition, valuable insights are extracted from extensive datasets, including user interactions and preferences. The incorporation of big data analytics, machine learning, and predictive modeling further refines recommender systems, allowing for real-time adaptation to evolving user tastes. The article emphasizes the significance of data mining in uncovering hidden patterns, improving the accuracy of recommendations, and enhancing the overall user experience. It discusses how data mining enhances recommender algorithms by unleashing hidden insights, analyzing user behavior, and optimizing decision-making processes, ultimately maximizing recommendation accuracy and improving user satisfaction.

کلیدواژه ها:

Data Mining ، Recommender Algorithms ، User Behavior Analysis ، Big Data Analytics ، Machine Learning