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Graded forecasting method based on user liveness

A technology for user activity and score prediction, which is applied in the field of score prediction based on user activity, and can solve problems such as ignorance

Inactive Publication Date: 2013-03-20
EAST CHINA NORMAL UNIVERSITY
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  • Summary
  • Abstract
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  • Claims
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AI Technical Summary

Problems solved by technology

In the traditional method, this kind of activity information, which can reflect the user's interest to a certain extent, is ignored.

Method used

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  • Graded forecasting method based on user liveness
  • Graded forecasting method based on user liveness
  • Graded forecasting method based on user liveness

Examples

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Embodiment Construction

[0041]The invention discloses a score prediction method based on user activity, which calculates the weight of user activity according to the number of user viewing times and the percentage of the type of viewing item, and combines the traditional collaborative filtering score prediction method to respond more reasonably user interests, which improves the quality of rating predictions. This method is an improvement on the traditional collaborative filtering algorithm.

[0042] Other characteristics, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0043] figure 1 A flow chart of the score prediction based on user activity is shown according to the first embodiment of the present invention. The collaborative filtering algorithm based on user activity provided by the present invention is applied in a recommendation system. First, it is deter...

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Abstract

The invention provides a graded forecasting method based on user liveness. The graded forecasting method based on the user liveness comprises the steps: (a) data pre-processing and sparsity processing, (b) guaranteeing liveness weighted value of each project of a user, (c) guaranteeing a similar user according to the liveness weighted value, and (d) forming final graded forecasting according to the liveness weighted value. A traditional collaborative filtering graded forecasting method is improved according to the user liveness, and the graded forecasting method based on the user liveness has the advantages of more accurately excavating user interests and having higher forecasting quality.

Description

technical field [0001] The invention relates to the field of collaborative filtering methods used in recommendation systems, in particular to an improved collaborative filtering method, which is a user score prediction method based on user activity. Background technique [0002] Collaborative filtering is the most commonly used technique in current recommendation systems. Items or objects are defined by the attributes of relevant features, and the system learns the user's interest based on the characteristics of the user's evaluation object, recommends according to the matching degree between the user's profile and the item to be predicted, and strives to recommend products similar to the products he liked before. [0003] The collaborative filtering system recommended from the user's point of view has the following advantages: [0004] 1. Ability to filter information that is difficult for machines to analyze automatically, such as artwork, music, etc. [0005] 2. Sharing...

Claims

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Application Information

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IPC IPC(8): G06Q30/02
Inventor 宋树彬崔永利吴奔斌霍晓骏王伟杰林雨薇贺樑杨燕
Owner EAST CHINA NORMAL UNIVERSITY
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