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This article discusses the application of the BP neural network algorithm in recommendation systems. It explains the design idea of the algorithm, which involves converting discrete high-dimensional features into continuous features with a constant length through a feature embedding operation, and then extracting features through multiple fully connected layers. It also explains the equations used to determine the number of hidden layers in three-layer neural networks, the activation function of the network’s hidden layer, and the weights and thresholds. Finally, it explains how the trained neural network can obtain the ideal output value by approximating new input.
