Cloud computing based real-time mass user behavior analyzing method and system

Abstract

The invention discloses a cloud computing based real-time mass user behavior analyzing method and system. User behaviors and context data are collected by a client end in real time and pre-processed and clustered on the basis of a MapReduce model; ontology data are subjected to reasoning, and latest interests of users are analyzed in real time; a track recurrence algorithm on the basis of a user behavior context is provided for track filling; interest similarity of users is computed by a cosine factor method, an interest similarity matrix is established; a Markov transfer matrix and a collaborative filtering based Markov recommendation model are established to realize effective and precise recommendation. The user behaviors and context information are subjected to model establishment via ontology, and semantic-level sharing and reusing of large-scale behavior information are achieved via an Hbase (hadoop database) based ontology memory mode. Technologies of cloud-computing, the ontology, reasoning and knowledge discovering are combined, and problems in instantaneity, efficiency, large-scale memorizing and intelligentization in mass user behavior analysis are solved.

Description

technical field [0001] The present invention relates to user behavior analysis, and specifically refers to a method and system for real-time analysis of mass user behavior based on cloud computing. Background technique [0002] User behavior analysis is to analyze the rules and personalized habits of users when using network resources. Its purpose is to monitor, predict, and even control user behavior. User interest is the specific performance of users' personalized characteristics for network resources. Today, with the rapid development of the Internet, analyzing user behavior, extracting user interest, and understanding its characteristics, on the one hand, user personalized customization and push can be carried out to provide more active and intelligent services for website visitors; on the other hand, From the different manifestations of user behavior, discovering their interests and preferences can optimize the organizational relationship between pages and improve the s...

AI Technical Summary

Problems solved by technology

[0003] For the analysis of user behavior, a lot of research has been done at home and abroad, but there are some problems: first, most of them focus on mining WEB logs, but these logs are not enough to describe the situation when users visit the website in time, it is necessary to collect real-time Combining the behavior and context information during operation can maximize the reproduction of the real scene of the user browsing the web page, extract a comprehensive user behavior track, and provide effective data protection for the analysis of user behavior

At present, most user behavior analysis systems use relational database technology and traditional data processing methods, which cannot meet the needs of efficient analysis of massive data.

In addition, for user behavior modeling, most scholars at home and abroad use Bayesian network models and vector models, which have a single dimension and lack of hierarchy, which is not conducive to information sharing, reuse, and reasoning and analysis of semantic-level behavior data.

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