Quick recommendation method in online social network labeling system

Abstract

The invention discloses a quick recommendation method in an online social network labeling system, belongs to the field of personalized recommendation and data mining, and solves the problems of ignorance of computing cost due to a conventional algorithm or mere pursuit of high accuracy or ignorance of accuracy and personalization due to mere pursuit of a simple model and quick computing. According to the method, a rough clustering algorithm that does not need to be iterated to be convergent is essentially a process of dividing users into clusters quickly according to user similarity indexes, and a user based collaborative filtering recommendation algorithm is also a process of performing recommendation according to the user similarity, so that high accuracy and individuality can be ensured; the time overhead of computing is reduced on the premise of ensuring the accuracy and individuality; on the basis of ensuring the accuracy and individuality of a recommendation algorithm in a recommendation system, the system time overhead is reduced; and therefore, the method has the characteristics of high accuracy, high individuality and low system time overhead.

Description

technical field [0001] The invention belongs to the fields of personalized recommendation and data mining. Background technique [0002] Acronyms and key term definitions: [0003] Accuracy: Refers to the similarity between the resources or items recommended by the recommendation system and the resources or items actually liked or purchased by users in the system. This indicator is used to describe the recommendation ability and actual utility of the recommendation system. [0004] Personalization: Refers to the recommendation system recommending corresponding resources or items according to the different characteristics of each user. Personalization is highly related to accuracy. [0005] System time overhead: refers to the sum of the time overhead for the recommendation system to perform data cleaning based on the existing data in the online system and the time overhead for calculation based on the cleaned data and related recommendation algorithms. [0006] Timeliness: ...

AI Technical Summary

Problems solved by technology

[0011] Aiming at the deficiencies of the prior art, the present invention provides a fast recommendation method in an online social network tagging system, which solves the problem that traditional algorithms either pursue high accuracy and ignore calculation costs, or pursue simple models and quickly calculate while ignoring accuracy Rate and Personalized Questions

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