An Agent Based Method for Web Page Prediction

Mukhopadhyay, Debajyoti; Mishra, Priyanka; Saha, Dwaipayan

doi:10.1007/978-3-540-72830-6_23

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…Prediction [1][2] of future requests remains an open challenge for researchers. Several approaches was configured in recent past for Web based prediction using sequential navigation patterns from server log files, combined with Web site topology.…”

Section: Introductionmentioning

confidence: 99%

A new approach in dynamic prediction for user based web page crawling

Kundu

Guha

Mitra

et al. 2010

Proceedings of the International Conference on Management of Emergent Digital EcoSystems

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Maximum available Web prediction techniques typically follow Markov model for Web based prediction. Everybody knows that there are lots of Web links or URLs on any Web page. So, it is very hard to predict the next Web page from the huge number of Web links. Existing approaches predict successfully on the private (personal) computer using different Markov models. In case of public (like cyber cafe) computers, prediction can not be done at all, since many people use the same machine in this type of scenario. In this paper, we propose a new policy on Web prediction using the dynamic behavior of users. We demonstrate four procedures for Web based prediction to make it faster. Our technique does not require any Web-log or usage history at client machine. We are going to use the mouse movement and its direction for the prediction of next Web page. We track the mouse position and its respective direction instead of using Markov model. In this research work, we introduce a fully dynamic Web prediction scheme, since Web-log or any type of static or previous information has not been utilized in our approach. In this paper, we try to minimize the number of Web links to be considered of any Web page in runtime for achieving better accuracy in dynamic Web prediction. Our approach shows the step-wise build-up of a solid Web prediction program which is appropriate in both the private as well as public scenario. Overall, this method shows a new way for prediction using dynamic nature of the respective users.

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Section: Introductionmentioning

confidence: 99%

A new approach in dynamic prediction for user based web page crawling

Kundu

Guha

Mitra

et al. 2010

Proceedings of the International Conference on Management of Emergent Digital EcoSystems

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Dynamic Web Prediction Using Asynchronous Mouse Activity

Kundu

2012

Computational Social Networks

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A Comparison of Scoring Metrics for Predicting the Next Navigation Step With Markov Model-Based Systems

Borges

Levene

2010

Int. J. Info. Tech. Dec. Mak.

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The problem of predicting the next request during a user's navigation session has been extensively studied. In this context, higher-order Markov models have been widely used to model navigation sessions and to predict the next navigation step, while prediction accuracy has been mainly evaluated with the hit and miss score. We claim that this score, although useful, is not sufficient for evaluating next link prediction models with the aim of finding a sufficient order of the model, the size of a recommendation set, and assessing the impact of unexpected events on the prediction accuracy. Herein, we make use of a variable length Markov model to compare the usefulness of three alternatives to the hit and miss score: the Mean Absolute Error, the Ignorance Score, and the Brier score. We present an extensive evaluation of the methods on real data sets and a comprehensive comparison of the scoring methods.

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An Agent Based Method for Web Page Prediction

Cited by 4 publications

References 4 publications

A new approach in dynamic prediction for user based web page crawling

A new approach in dynamic prediction for user based web page crawling

Dynamic Web Prediction Using Asynchronous Mouse Activity

A Comparison of Scoring Metrics for Predicting the Next Navigation Step With Markov Model-Based Systems

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