Recommendation method based on label semantic normalization

Abstract

The invention relates to a recommendation method based on label semantic normalization. The method comprises the steps that a user-defined label is preprocessed to obtain the preprocessed label, and label semantic similarity is obtained according to calculation; a label resource matrix is obtained according to the preprocessed label, and then label resource co-occurrence similarity is obtained through calculation; linear fusion similarity is obtained through calculation according to the label semantic similarity and the label resource co-occurrence similarity, the linear fusion similarity is subjected to clustering operation to obtain label data after semantic normalization by a user, and collaborative filtering recommendation is performed in combination with the label data obtained after semantic normalization by the user. With respect to a plenty of redundant labels or labels with inaccurate semantic expression in a previous label system, by means of label normalization, the semantic expression of the normalized labels can be clearer; in a recommendation system, recommendation quality, namely accuracy and efficiency can be improved, and recommendation time can be shortened.

Description

technical field [0001] The invention relates to the field of personalized recommendation methods, in particular to a personalized recommendation technology for building a user interest model based on tags. Background technique [0002] The rapid development of the Internet has greatly changed the way people search for information and share knowledge, and at the same time changed the way of communication and interaction between people. With the continuous growth of information, users need to spend a lot of time and energy to find the resources they need from the massive information. This phenomenon is called the problem of information overload. So the recommendation system was born. The recommendation system is a technology that intelligently selects a small number of resources that reflect user preferences from massive information resources and recommends them to users based on user preferences and some historical data, thereby better solving the problem of information over...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the disadvantages of the recommendation system itself. The disadvantages include the data sparsity of the system, cold start problems and stability problems. These disadvantages make it difficult for the recommendation system to make correct recommendations based on the historical behavior of users Problems such as Internet information overload and the complexity and fragmentation of tags in the social tagging system, which are not easy to produce semantic associations,

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