Method for solving collaborative filtering recommendation data sparsity based on neural network

Abstract

The invention provides a method for solving collaborative filtering recommendation data sparsity based on a neural network. The method for solving collaborative filtering recommendation data sparsity based on the neural network adopts generalized regression of neural network (GRNN) and conducts full filling on sparse data by a train network model and score prediction. The method for solving collaborative filtering recommendation data sparsity based on the neural network comprises the following steps: before conducting the GRNN training, conducting screening on input variables of the neural network by adopting mean impact value (MIV), choosing characteristic values having great impact on output as valid input variables; using the valid input variable to construct the input matrix of the GRNN; adopting Kfold cross validation circulation to find out an optimal spread value of the GRNN; using the optimal spread value and the corresponding input matrix and output matrix to conduct GRNN training; using the trained GRNN to conduct score prediction on a sparse score matrix; and replacing non-scored data of the sparse score matrix with predicted score values. The method for solving collaborative filtering recommendation data sparsity based on the neural network can conduct fully filling on sparse recommendation data, solve the data height sparsity problem most outstanding in existing collaborative technology, and enable recommendation result to be accurate.

Description

technical field [0001] The invention belongs to the technical field of artificial neural network and personalized recommendation, and more specifically relates to a method for solving the sparsity of collaborative filtering recommendation data based on neural network. Background technique [0002] In the modern information society, various industries will generate massive information data after a period of accumulation. How to effectively extract useful information from massive data has set off a research boom in personalized recommendation technology. As the main recommendation technology, collaborative filtering technology has attracted much attention and has been successfully applied to various recommendation systems. However, with the continuous expansion of resource types and the increasing number of users, the data matrix used for evaluation is becoming more and more sparse, which seriously affects the recommendation quality. [0003] Neural network is an algorithmic ...

AI Technical Summary

Problems solved by technology

However, with the continuous expansion of resource types and the increasing number of users, the data matrix used for evaluation is becoming more and more sparse, which seriously affects the quality of recommendation.

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