Recommended simulation of mixed attribute information under a recursive neural network
The substantial increase in the amount of information makes it impossible for users to extract useful in-formation from the recommended data.As a result,a recommendation algorithm for mixed attribute information based on a recursive neural network was proposed.At first,the data preprocessing method was adopted to delete the mixed attribute information without any information score,and thus to mine the relationship between users and mixed attribute information.Then,the graded mixed attribute information was combined with the eXtreme Gradient Boosting(XGBoost)algorithm to classify the mixed attribute information.Moreover,a recurrent neural network model was con-structed,and then the gradient descent method was adopted to train the model,thus obtaining the probability value of each mixed attribute information.Finally,these values were arranged in order,thus forming a recommendation list that was directly pushed to users.Experimental results show that the HR value is improved,and the mean value of NDCG is 0.805,so the proposed method comprehensively improves the accuracy of the recommendation results.
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