Development of Association Rule Based Prediction Model for Web Documents
Sachin Kumar Sharma, Simple Sharma, Anupriya Jain, Rashmi Aggarwal, Seema Sharma, Manav Rachna · 2012
The rapid expansion of the WWW has created an unprecedented opportunity to disseminate and gather information online. Electronic Commerce is emerging as the biggest application of WWW. As this trend becomes stronger and stronger, there is much need to study web-user behaviors to better serve the users and increase the value of enterprises. One important data source for this study is the web-log data that traces the user’s web browsing actions. From the web logs, one can build prediction models that predict with high accuracy the user’s next request based on past behavior. To do this with the traditional association rule methods will cause a number of serious problems due to extremely large data size and the rich domain knowledge that must be applied. Most web log data are sequential in nature and exhibit the “most recent-most important” behavior. To overcome this difficulty, we examine two dimensions of building prediction models. This paper proposes a better overall method for prediction model representation and refinement.