Matrix decomposition method and device integrating convolutional neural network and explicit feedback and electronic equipment

Abstract

The invention relates to a matrix decomposition method and device integrating a convolutional neural network and explicit feedback and electronic equipment. The method comprises the following steps: expressing a user description document of an article as a word vector matrix; mapping the score set of the user to the article into a binary vector; obtaining hidden factors of the article and the userthrough the convolutional attention neural network and the stacked noise reduction automatic encoder respectively; Solving a vector inner product of the hidden factors of the article and the hidden factors of the user to obtain a prediction scoring matrix; and optimizing parameters of the convolutional attention neural network and the stacked noise reduction automatic encoder according to a lossfunction between the prediction scoring matrix and the real scoring matrix. The matrix decomposition method fusing the convolutional neural network and the explicit feedback is good in interpretability, can alleviate the cold start problem of an article, can improve the data sparsity problem, and improves the accuracy of score prediction.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a matrix decomposition method, device and electronic equipment that integrate convolutional neural network and explicit feedback. Background technique [0002] The recommendation method based on matrix decomposition is a commonly used basic method at present. By decomposing the rating matrix of a given user on an item, the product of two low-rank matrices is used to approximate the original rating matrix. The goal of the approximation is to make the predicted rating matrix The square error between it and the original scoring matrix is ​​the smallest. The two low-rank matrices are the feature matrices of users and items, and these feature vectors in the matrix can be simply understood as user preferences for different attributes. Compared with content-based filtering methods, matrix factorization has better domain adaptability and can deal with unstructured data, ...

AI Technical Summary

Problems solved by technology

[0003] The existing rating prediction regards the user's rating information on the item as a simple rating, and does not think deeply about the deep meaning contained in the user's rating of different items and the ratings of different users for the same item from the perspective of users and items. Simple Matrix factorization cannot add text features, and cannot deeply understand the user's comment text on items, so it cannot solve the interpretability problems of cold start and recommendation

[0004] Convolutional neural networks can be used to extract hidden features of text, but convolutional neural networks often ignore the feature extraction of single words, and different words in the text have different importance for scoring predictions, and most of the work does not consider different word pairs Variability in rating predictions

Images