CP Decomposition, or Canonical Polyadic Decomposition, is a mathematical technique used to break down a multi-dimensional array, known as a tensor, into simpler, interpretable components. This method expresses the tensor as a sum of rank-one tensors, making it easier to analyze and manipulate data in various fields, such as statistics and machine learning. The primary goal of CP Decomposition is to reveal underlying patterns and structures within the data. By decomposing a tensor, researchers can identify relationships among different dimensions, facilitating tasks like data compression, feature extraction, and collaborative filtering in recommendation systems.