Research on Application of User Navigation Pattern Mining Recommendation

In the personalized recommendation systems, which are based on web usage mining, association-mining technology was applied to predict user-browsing behavior and this method has gained much attention. However, the resulting association patterns did not perform well in predicting future browsing patterns. The paper carried a research on the most popular method of data preprocess, then the dividing historical navigation sessions into frames of sessions based on day interval was presented according to the real need of the cluster algorithm, as result of that, the efficiency of the data preprocess was enhanced, and got a sound trail conclusion. And then, a new recommendation method was proposed to be applied to web log mining by integrating user clustering and association rule mining techniques to improve the effectiveness of electronic commerce recommendation. The experiment results from real Web server logs showed that the method could improve the precision rate and F rate of evaluation criteria effectively.