Joint deep recommendation model based on attention recurrent neural network
A joint deep recommendation model based on attention recurrent neural network is designed to recommend projects that meet user interests and preferences.The attention mechanism with double layers is set in the network.The designed model consists of five parts.The input matrix of the joint deep recommendation model is generated in the input layer.By the sequence coding layer,the semantics of the project comment text is encoded forward and backward to obtain the hidden state output.The hidden state output is input into the attention mechanism with double layers to extract the project features.The fully-connected layer is used to extract user preference features.An interaction model between projects and users is established in the prediction layer,so as to obtain project ratings and recommend high-rated projects for users.In order to improve the accuracy of the model,the combined loss function is established based on the weighted integration of MSE loss function,CE loss function and RK loss function.The deep joint training model is trained to improve the recommendation performance of the model.The simulation results show that the proposed method has good recommendation effect,so it can adapt to the changing market demand and user behavior.
attention mechanism with double layersrecurrent neural networkuser preferencecombined loss functioninteraction modeljoint deep recommendation model
万方数据
维普
