Prediction of User Model based on Markov Chains

Abstract

Aiming at the lack of user model prediction methods, we propose a user model prediction algorithm based on Markov chain and Bayesian theorem (MCBT). The flow chart of the algorithm is as follows: firstly, establish the correlation matrix of web page types to get the degree of correlation among web page types; secondly, use Markov chain to predict the type of web pages that users will visit; thirdly, use the Bayesian theorem to predict the specific web pages to be visited within the range of candidate web pages; finally, predict the user behavior characteristics of each page based on the existing user behavior characteristics data. The user model predicted by this algorithm is similar to the original user model.