Adaptive customized recommendation method based on users and articles

Abstract

The invention discloses an adaptive customized recommendation method based on users and articles. The method comprises two stages of training and customized recommendation. For the training stage, firstly, data including the user personal information, user behavior characteristics and object evaluation of the users is acquired through a platform; similar users are clustered according to the user data, a mean difference matrix of the object evaluation of the users is calculated, a prediction model based on user clustering is established, and an evaluation prediction error of the model for all the objects is calculated; similarities among objects are calculated according to attributes of the objects, mean object evaluation difference of the users is calculated, a prediction model is established, an adaptive prediction model based on the users and the objects is formed. For the customized recommendation stage, firstly, user attribute clustering is determined, the adaptive prediction model integrated with the users and the objects is utilized, evaluation of the users for the objects is predicted, and the objects with high prediction evaluation are recommended to the users. The method is advantaged in that the method has adaptive capability and higher accuracy compared with a traditional customized recommendation method.

Description

technical field [0001] The invention belongs to the technical field of personalized recommendation based on big data, and in particular relates to an adaptive personalized recommendation method based on users and items. Background technique [0002] The rapid development of Internet technology makes a large amount of information explosively presented to the public. The information explosion will reduce the utilization rate of information, which is the so-called "information overload". Personalized recommendation is a hot field of current research. It can find users' needs in a large amount of redundant information. Therefore, it can improve user experience and enterprise marketing. Through relevant machine learning and data mining technologies, the recommendation system mines the user's purchasing tendency and recommends items that the user may be interested in to the user. A good recommendation system can dig out the potential consumption preferences of users and provide ...

AI Technical Summary

Problems solved by technology

However, whether it is only considered from the perspective of users or only from the perspective of items, practice has proved that the accuracy of prediction is difficult to satisfy

[0004] In the prior art, there is no fusion of the two to form an adaptive personalized recommendation method based on users and items to achieve high prediction accuracy

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