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A Hybrid Approach for User Request Prediction using Hidden Markov Model

Author(s):

Shraddha Chouhan , Indore Institute of Science and Technology-II; Pradeep Baniya, Indore Institute of Science and Technology-II

Keywords:

Web Usage Mining, Semantic Web, Domain, Sequential Pattern Mining, Recommender Systems, and Markov Model, Prediction, web log

Abstract

This web log contains lot of information so it is preprocessed before modeling. The web log file is preprocessed and converted into the sequence of user web navigation sessions. The web navigation session is the sequence of web page navigated by a user during time window. The user navigation session is finally modeled through a model. Once the user navigation model is ready, the mining task can be performed for finding the interesting pattern. Modeling of web log is the essential task in web usage mining. The prediction accuracy can be achieved through a modeling the web log with an accurate model to improve the performance of the servers, caching is used where the frequently accessed pages are stored in proxy server caches. Pre-fetching of web pages is the new research area which when used with caching greatly increases the performance. In this paper, a better algorithm for predicting the web pages is proposed. Clustering of web users according to their location using clustering is done and then each cluster is mined using FP-Growth algorithm to find the association rules and predict the pages to be pre- fetched for storing in cache.

Other Details

Paper ID: IJSRDV4I70522
Published in: Volume : 4, Issue : 7
Publication Date: 01/10/2016
Page(s): 847-851

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