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A Neoteric Web Recommender System based on Approach of Mining Frequent Sequential Pattern from Customized Web Log Preprocessing
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 69 - Number 24 |
| Year of Publication: 2013 |
| Authors: Manisha Valera, Kirit Rathod, Uttam Chauhan |
10.5120/12119-8197
|
Manisha Valera, Kirit Rathod, Uttam Chauhan . A Neoteric Web Recommender System based on Approach of Mining Frequent Sequential Pattern from Customized Web Log Preprocessing. International Journal of Computer Applications. 69, 24 ( May 2013), 16-21. DOI=10.5120/12119-8197
Abstract
A real world challenging task of the web master of an organization is to match the needs of user and keep their attention in their web site. So, only option is to capture the intuition of the user and provide them with the recommendation list. Web usage mining is a kind of data mining method that provide intelligent personalized online services such as web recommendations, it is usually necessary to model users' web access behavior. Web usage mining includes three process, namely, preprocessing, pattern discovery and pattern analysis. After the completion of these three phases the user can find the required usage patterns and use this information for the specific needs. The data abstraction is achieved through data preprocessing. The aim of discovering frequent sequential access patterns in Web log data is to obtain information about the navigational behavior of the users. In the proposed system, an efficient sequential pattern mining algorithm is used to identify frequent sequential web access patterns. The access patterns are retrieved from a Graph, which is then used for matching and generating web links for recommendations.
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