Open AccessPredicting User’s Web Navigation behaviour using AMD and HMM Approaches
Author(s) -
Rita Roy
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1074/1/012031
Subject(s) - computer science , hidden markov model , session (web analytics) , web page , cluster analysis , web navigation , web server , world wide web , web mining , web log analysis software , static web page , web service , information retrieval , data mining , the internet , artificial intelligence
The academics were introducing various techniques in web usage mining to reduce user latency time to improve their web performance. The server-side web log data helps to identify the most appropriate pages, based on the user’s request. Analyzing web log data creates difficulties since it comprises exhaustive web page data. This paper proposes a new technique in which the web log data we can preprocess in order to extract sequence and navigation patterns useful to predict. In this paper, we cluster users into communities labelled by the websites most frequently visited, to discover their preferences and to describe website reorganisation. The URL sequence navigated by a user during a session. Session denotes a user’s browsing pattern. Multiple users’ sessions are clustered using the hierarchical clustering technique to analyse the sequence in their navigation patterns. The two model predictions are Adaptive Mahanabolis Distance (AMD) and Hidden Markov model (HMM), providing a list of web pages of interest to the user. We validate the proposed framework on NASA, Clarknet and serc datasets web log files.