User Navigation Behavior Mining Using Multiple Data Domain Description

User Navigation Behavior Mining (UNBM) mainly studies the problems of extracting the interesting user access patterns from user access sequences (UAS), which are usually used for user access prediction and web page recommendation. Through analyzing the real world web data, we find most of user access sequences carrying hybrid features of different patterns, rather than a single one. Therefore, the methods that categorize one access sequence into a single pattern, can hardly obtain good quality results. Due to this problem, we propose a multi-task learning approach based on multiple data domain description model (MDDD), which simultaneously captures correlations among patterns and allowing categorizing one UAS into more than one patterns.The experimental results show that our method achieves high performances in both precision and recall by virtue of using MDDD model.