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Analysis of Users Web Browsing Behavior Using Markov Chain Model


Affiliations
1 Department of Mathematics and Statistics, Sagar University, Sagar-470003, M.P., India
2 International Institute of Professional Studies, D.A.V.V., Indore (M.P.), India
 

In present days of growing information technology, many browsers available for surfing and web mining. A user has option to use any of them at a time to mine out the desired website. Every browser has pre-defined level of popularity and reputation in the market. This paper considers the setup of only two browsers in a computer system and a user prefers to any one, if fails, switches to the other one. The behavior of user is modeled through Markov chain procedure and transition probabilities are calculated. The quitting to browsing is treated as a parameter of variation over the popularity. Graphical study is performed to explain the inter relationship between user behavior parameters and browser market popularity parameters. If rate of a company is lowest in terms of browser failure and lowest in terms of quitting probability then company enjoys better popularity and larger user proportion.

Keywords

Markov Chains (MC), Transition Probability Matrix (TPM), Quality of Service (QOS), Browser Failure (BF).
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  • Analysis of Users Web Browsing Behavior Using Markov Chain Model

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Authors

Diwakar Shukla
Department of Mathematics and Statistics, Sagar University, Sagar-470003, M.P., India
Rahul Singhai
International Institute of Professional Studies, D.A.V.V., Indore (M.P.), India

Abstract


In present days of growing information technology, many browsers available for surfing and web mining. A user has option to use any of them at a time to mine out the desired website. Every browser has pre-defined level of popularity and reputation in the market. This paper considers the setup of only two browsers in a computer system and a user prefers to any one, if fails, switches to the other one. The behavior of user is modeled through Markov chain procedure and transition probabilities are calculated. The quitting to browsing is treated as a parameter of variation over the popularity. Graphical study is performed to explain the inter relationship between user behavior parameters and browser market popularity parameters. If rate of a company is lowest in terms of browser failure and lowest in terms of quitting probability then company enjoys better popularity and larger user proportion.

Keywords


Markov Chains (MC), Transition Probability Matrix (TPM), Quality of Service (QOS), Browser Failure (BF).