Web Page Recommendation Approach Using Weighted Sequential Patterns

To predict the web users navigation with the help of Web Usage Mining techniques. This book primarily focuses on real world applicability of a new Web page Recommendation approach using both Weighted Sequential patterns and Markov probabilistic model. To find the Weighted Sequential patterns, the existing PrefixSpan algorithm has been modified by incorporating the weightage constraints such as spending time and recent visiting. Once the weighted sequential patterns are identified, a Patricia-trie based tree is constructed. Finally from the constructed pattern tree, the recommendation of web pages to the current users is done with the help of Markov probabilistic model. This model enables the reasoning and computation as intractable to identify the future access web pages based on the user past browsing interests.