User behavior analysis method based on Bayesian classification algorithm

Abstract

The invention discloses a user behavior analysis method based on a Bayesian classification algorithm, comprising the following steps: S1 collecting user operation records; S2, analyzing the user operation record, calculating the occurrence probability of each user operation and the conditional probability between each user operation to train a Bayesian classifier for judging the next operation ofthe user according to the current user operation; 3, inputting a current operation of a user to that Bayesian classify for judging the next operation of the user; S4 repeating the step S3 according tothe next operation of the user until the next operation of the user is the designated end operation to obtain a sequence of user behaviors. With the advantages of a large number of data samples, improve the accuracy and efficiency of analysis of the technical effect.

Description

Technical field [0001] The present invention relates to the technical field of data analysis, in particular to a user behavior analysis method based on Bayesian classification algorithm Background technique [0002] Today's service industry attaches great importance to the optimization of service processes, and the results of user behavior analysis are often used as the basis for optimization. [0003] Traditional user behavior analysis mostly relies on manual analysis, which is inefficient. When the amount of user behavior data is small, it can still meet the needs, but at the same time it also brings the problem of insufficient accuracy. However, with the development of e-commerce in China, More and more users adopt convenient forms such as webpages and mobile apps to receive services, and their behavior data is also easier to obtain and record. This makes the source of user behavior data and the amount of data greatly increased, and manual analysis The method simply cannot make...

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Problems solved by technology

[0003] The traditional user behavior analysis mostly relies on manual analysis, which is inefficient. When the amount of user behavior data is small, it can still meet the needs, but at the same time it also brings the problem of inaccurate results; however, with the development of e-commerce in China, More and more users accept services through convenient forms such as webpages and mobile APPs, which makes their behavior data easier to obtain and record. The method cannot make good use of a large number of data samples at all, and there is an urgent need for a user behavior analysis method that can give full play to the advantages of a large number of data samples and improve analysis efficiency and accuracy.

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