Collaborative filtering method based on user project classes and scoring reliability

Abstract

The invention discloses a collaborative filtering method based on user project classes and scoring reliability, and the method comprises the steps: building a user scoring matrix and a user project class feature matrix through user scoring data and project information, and adding a common scoring penalty factor according to the user scoring matrix to calculate the similarity of user scores; according to the user project feature class matrix, calculating the similarity of user project preference is calculated by accessing the number of times of project feature classes and enabling the project feature class score to be larger than the average value number, on the basis, calculating the total similarity of users and then predicting on the basis of neighbor scoring reliability, unscored projects to generate recommended projects by establishing a nearest neighbor set. According to the method, the real interest preference of the user can be obtained more accurately, personalized recommendation is conducted on the designated user, and therefore the quality of a recommendation system is improved.

Description

technical field [0001] The invention relates to the technical field of big data mining and recommendation, in particular to a collaborative filtering method based on user item categories and scoring reliability. Background technique [0002] As an important supporting method of the recommendation system, the recommendation method is the core technology of the recommendation system, so it is necessary to study how to improve the accuracy of the recommendation method and improve the user experience. Recommendation methods have been widely studied and applied, such as collaborative recommendation, content-based recommendation methods, association rule-based recommendation algorithms, tag-based recommendation algorithms, etc. At present, the collaborative recommendation method is one of the recommendation methods with high accuracy and the most successful research and application in the recommendation system. The collaborative filtering recommendation method is based on the use...

AI Technical Summary

Problems solved by technology

[0009] The recommendation method is very important for the accuracy of the recommendation system and the user experience of the recommendation system. In view of the shortcomings of the existing recommendation algorithm, such as low accuracy, poor user experience, and incomplete consideration of the user interest calculation model, the present invention proposes a method based on The collaborative filtering method of user item class and rating reliability can take into account the difference between user item class ratings and rating standards and the reliability of user neighbor ratings when constructing user interest models

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