Network resource personalized recommended method based on ultrafast neural network

Abstract

The invention belongs to the field of network resource management, relates to the cooperative filtration technique of network resources and discloses a network resource personalized recommended method based on an ultrafast neural network. The network resource personalized recommended method based on ultrafast neural network comprises the following steps: firstly, data preprocessing, reading information from the journal files of a system user and generating a global user interested matrix, exchanging the global user interested matrix into a single user interested matrix of a current user, and then transforming and reducing dimensionality to mark out a training set A1 and a predicting set A2, secondly, model training, building an interest predicting model with single hidden layer neural network SLFN s structure for a target user, adopting ultrafast learning machine technique to carry out training on the training set A1 and getting various connection power values and hidden layer threshold values of the neural network model with single hidden layer, thirdly, prediction recommending, utilizing the obtained predicting model to calculate the scoring values of every resource in the predicting set A2 given by the target user and then recommending the first several resources with highest predicting scores to the target user.

Description

technical field [0001] The invention belongs to the field of network resource management, relates to collaborative filtering technology of network resources, in particular to a network resource personalized recommendation method based on an extremely fast neural network model. Background technique [0002] Currently, collaborative filtering technology is the most popular personalized recommendation technology. The research on user modeling and user interest prediction based on it is concentrated in the field of Web Usage Mining, and its data source is mainly the Web log recorded on the server side—user interest information. Commonly used collaborative filtering technologies mainly include the following three types: ① User-based collaborative filtering technology, which recommends resources for users who are interested in similar users; ② Item-based collaborative filtering technology, which recommends resources for users Recommend resources that are similar to the resources ...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the deficiencies of the above-mentioned prior art, and propose a collaborative filtering method based on an extremely fast neural network, which can solve the problem that the information recommendation quality of collaborative filtering technology is greatly reduced due to high-dimensional sparse data, and the time-consuming system calculation Problems that are too large to meet the real-time performance requirements of online users for the system

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