Method for information recommendation in could environment based on data semantics

Abstract

The invention relates to a method for information recommendation in a could environment based on data semantics. The method achieves the information recommendation through a basic data and user preference information semantization module, a could environment mass semantic information indexing module and a semantic calculation based information recommendation module to achieve the information recommendation, wherein the basic data and user preference information semantization module obtains basic data and user preference information through a cloud platform and conducts semantization description on the basic data and the user preference information to form a basic data and user preference information body library, the could environment mass semantic information indexing module establishes an indexing structure for semantic information and performs index division and regrouping when indexing nodes are overload, the semantic calculation based information recommendation module conducts semantic calculation on the basic data and user preference information body to obtain an information recommendation result. Compared with the prior art, the method for information recommendation in the could environment based on data semantics has the advantages of being good in real-timeliness, high in robustness and recommendation quality and the like.

Description

technical field [0001] The invention relates to a data processing method in a cloud environment, in particular to an information recommendation method based on data semantics in a cloud environment. Background technique [0002] The rapid development of the Internet and the Internet of Things technology makes massive information presented to us at the same time, for example, there are nearly a million books on Dangdang, millions of movies on Netflix, and millions of new listings on eBay every day , and the del.icio.us community network has more than 1.5 billion web page collections, and the information overload is explosive. As a result, end users cannot accurately and efficiently find objects of interest. Therefore, for enterprises, the problem of information overload will seriously reduce their own economic benefits and market competitiveness. Currently, information recommendation system is one of the most effective tools to solve the problem of information overload. In ...

AI Technical Summary

Problems solved by technology

The biggest flaw of the content-based recommendation method is that it must analyze the content information of the product, so it can do nothing about music, images, videos, etc., and cannot analyze the quality of its information

The biggest defect of the collaborative filtering recommendation method is that with the increase of the number of products and users, the time complexity of the method will increase exponentially, which makes the system unable to recommend suitable products to users in real time or quickly.

[0007] However, we found that with the emergence of massive data and the maturity of Web 2.0 technology, the existing information recommendation technology faces at least three serious problems: (1) The information of a large number of users and products on the website is dynamically changing, which leads to The existing recommendation method needs to spend a huge amount of calculation to remodel, which seriously affects the real-time performance of the recommendation results; (2) The openness of the Web 2.0 network makes the website often attacked by malicious users, and the pressure of user traffic leads to software modules However, the robustness of existing information recommendation methods is weak, which makes the system easy to be paralyzed; (3) Existing information recommendation methods usually only model the user's current preference, and do not care about the evolution of preference , which will largely affect the quality of recommendation and the effect of personalized adaptation

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