A Method of Constructing Data Complementary Value

Abstract

The invention discloses a method for constructing a data complement value. The method is applied to electronic equipment and comprises the steps that the electronic equipment acquires user-project behavioral data; the criticality of neighbors of a project is calculated based on the user-project behavioral data; a neighbor set of the project is divided into a fixed nearest neighbor set and a sampling neighbor set; a nearest neighbor sampling aggregation is constructed; the complement value with the user-project behavioral data missed is constructed based on the neighbor criticality corresponding to the fixed nearest neighbor set and the nearest neighbor sampling aggregation and the user-project behavioral data. According to the method, the technical problem of the prior art that the accuracy of a collaborative filtering model based on K neighbors can not be improved is solved, and the technical effect that the accuracy and coverage rate are both improved during missed behavioral value complementation is achieved.

Description

technical field [0001] The invention relates to the field of information technology, in particular to a method for constructing data supplementary values. Background technique [0002] In the era of big data, the scale of Internet information has grown explosively, which has brought about the problem of Internet information overload. Excessive information is presented at the same time, making it difficult for users to filter out the parts that are effective for individuals, and the information utilization rate is reduced instead. To deal with the problem of Internet information overload, it is necessary to use big data processing and analysis technology to analyze massive Internet data information, find out the relationship between users and data, and realize active information push for users in the big data environment, so as to solve information overload question. [0003] The collaborative filtering model based on K-nearest neighbors is a classic big data processing and ...

AI Technical Summary

Problems solved by technology

[0008] The embodiment of the present application solves the technical problem that the accuracy of the existing collaborative filtering model based on K-nearest neighbors in the prior art cannot be improved by providing a method for constructing data supplementary values, and realizes accurate supplementation of missing behavior values. Both degree and coverage have greatly improved technical effects

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