Hybrid recommendation algorithm based on deep neural network and probability matrix decomposition

Abstract

The invention relates to a hybrid recommendation algorithm based on a deep neural network and probability matrix decomposition. The method comprises steps of S10, collecting and preprocessing information; S20, establishing a deep neural network model and a probability matrix decomposition model; S30, obtaining a potential feature vector fusing real information of the user and the project accordingto the three steps; and S40, performing personalized recommendation on the user by utilizing the feature vector. The invention discloses a hybrid recommendation algorithm based on a deep neural network and probability matrix decomposition. The beneficial effects of the invention are as follows: the method is based on previous research; the user scoring matrix data is fully utilized; and meanwhile, feature extraction is performed on the user description information and the project description information by using a deep neural network to generate a user and project real feature set includinguser preference features, and performing iterative optimization processing on the probability matrix model and the features generated by the deep neural network by using maximum posteriori estimation.

Description

technical field [0001] The invention relates to the technical field of a hybrid recommendation algorithm based on a deep neural network and probability matrix decomposition, in particular to a hybrid recommendation algorithm based on a deep neural network and probability matrix decomposition. Background technique [0002] With the rapid development of Internet technology, the society has shown a state of data and information blowout, and people have gradually entered the era of big data from the era of information scarcity. In order to solve the problem of low availability of information caused by a large number of redundant information, so that users can effectively obtain the information they need, the recommendation system came into being. The personalized recommendation system recommends information and items that the user is interested in based on various information such as the user's purchase and consumption records, user and item characteristics, and evaluation of th...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a hybrid recommendation algorithm based on deep neural network and probability matrix decomposition, to solve the above-mentioned background technology. In many recommendation systems, collaborative filtering recommendation algorithm is undoubtedly the most popular recommendation algorithm, but The sparsity of the user rating matrix leads to the decline of the rating prediction accuracy of the traditional collaborative filtering recommendation algorithm, which affects the actual recommendation effect and reduces the quality of the recommendation.

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