Web Page Recommendation Approach Using Weighted Sequential Patterns And Markov Model

Web page recommendation aims to predict the user2019;s navigation through the help of web usage mining techniques. Currently, researchers focus their attention to develop a web page recommendation algorithm using the well known pattern mining techniques. Here, we have presented a web page recommendation algorithm using weighted sequential patterns and markov model. To mine the weighted sequential pattern, we have modified the prefixspan algorithm incorporating the weightage constraints such as, spending time and recent visiting. Then, the weighted sequential patterns are utilized to construct the recommendation model using the Patricia trie-based tree structure. Finally, the recommendation of the current users is done with the help of markov model that is the probability theory enabling the reasoning and computation as intractable. For experimentation, the synthetic dataset is utilized to analyze the performance of W-Prefixspan algorithm as well as web page recommendation algorithm. From the results, the memory required for the W-prefixSpan algorithm is less than 50% of memory needed for PrefixSpan algorithm

A least square approach to analyze usage data for effective web personalization

Each cluster is having users with similar browsing patterns. These clusters are useful in web personalization so that it communicates better with its users. Experimental results indicate that using the generated aggregate usage profiles with approximating clusters through least square approach effectively personalize at early stages of user visits to a site without deeper knowledge about them

Integrating recommendation models for improved web page prediction accuracy

[Abstract]: Recent research initiatives have addressed the need for improved performance of Web page prediction that would profit many applications, e-business in particular. Despite the various eforts so far, there is still room for advancement in this field. This paper endeavors to provide an improved prediction accuracy by using a novel approach that involves combining clustering, association rules and Markov models. Each of these frameworks has its own strengths and weaknesses and their integration proves to provide better prediction than using each technique individually