Users Interest Prediction Model - Based on 2nd Markov Model and Inter-transaction Association Rules

The 2 Markov Model and inter-transaction association rules are both known as key technologies for building user interest prediction models. The use of these technologies potentially improves the users surfing experience. The use of the 2 Markov Model increases the accuracy of predictions, but it does not cover all the data. Therefore, in this paper we propose a dual strategy for a user interest prediction model that includes the entire data set and improves the accuracy of inter-transaction association rules. The foundation of our dual strategy is a new method of building a database based on the degree of user interest. Secondly, we integrate the 2 Markov Model and inter-transaction association rules for predicting future browsing patterns of users. Experimental results show that this method provides more accurate prediction results than previous similar research.

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