Paper Details

The World Wide Web store, share, and distribute information in the large scale. There islarge number of internet users on the web. They are facing many problems like information overloaddue to the significant and rapid growth in the amount of information and the number of users. As aresult, how to provide web users with more exactly needed information is becoming a critical issue inweb applications. Web mining extracts interesting pattern or knowledge from web data. It is classifiedinto three types as web content mining, web structure, and web usage mining. Web usage mining is theprocess of extracting useful knowledge from the server logs. This useful knowledge can be applied totarget marketing and in the design of web portals. It may give information that is useful for improvingthe services offered by web portals and information access and retrieval tools. In this paper we areintroducing a new approach for web page recommendation and user profile generation. This approachmakes use of evolutionary biclustering technique for web page recommendation. We have applied it ontwo different datasets. One is clickstream data and other is web access log file of KSV University. Thefinal results are generated from optimal biclusters obtained from evolutionary biclustering.

Raval Pratiksha M., Mehul Barot, “A Web Page Recommendation system using GA based biclustering of web usage data”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 1, Issue 5, pp. 820-826, May 2014.