@article{MAKHILLAJIT20121165724,
    title = {Performance Analysis of Web Page Recommendation Algorithm Based on Weighted Sequential Patterns and Markov Model},
    journal = {Asian Journal of Information Technology},
    volume = {11},
    number = {6},
    pages = {270-275},
    year = {2012},
    issn = {1682-3915},
    doi = {ajit.2012.270.275},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2012.270.275},
    author = {K.},
    keywords = {Prefixspan,web page recommendation,weighted sequential pattern,patricia-trie,Markov Model},
    abstract = {Web usage mining techniques helps the users to predict the 
  required web page recommendations. In recent times there has been a considerable 
  significance given to sequential mining approaches to construct Web Page Recommendation 
  Systems. This study focuses on developing a web page recommendation approach 
  for accessing related web pages more efficiently and effectively using weighted 
  sequential pattern mining and Markov Model. Here, researchers have developed 
  an algorithm called, W-PrefixSpan that is the modification of traditional Prefixspan 
  algorithm including the constraints of spending time and recent visiting to 
  extract weighted sequential patterns. Then, by utilizing Weighted Sequential 
  Patterns Recommendation Model is constructed based on Patricia-trie data structure. 
  Later the web page recommendation of the current users is done with the help 
  of Markov Model. Experimentation is done with the help of synthetic dataset 
  and we present the performance report of web page recommendation algorithm in 
  terms of precision, applicability and hit ratio. The results have shown that 
  the precision of the algorithm is improved by 5% than the earlier algorithm. 
  Also, researchers have achieved high applicability in the support of 50% and 
  in terms of hit ratio, the proposed algorithm ensured that the performance is 
  considerably improved for various support values.}
    }