Abstract
A variety of recent trend predictions for the social Web see an evolution in the making. Social business thrives as more businesses would enter the social Web. Information technology (IT) departments would open up to social Web and adapt the new processes needed for opening up. Social media marketing would use social media differently. In this scenario, dynamic Web prediction comes into the picture for handling the real-time scenario in a smoother way. A typical Web prediction method follows Markov model. A Web page consists of several hyperlinks. Prediction requires complicated methodologies for selection of a particular hyperlink from the pool of hyperlinks of current Web page. Existing approaches forecast only on personal computer in a fruitful manner. In case of public computers, the same machine is used by different users at different time instance. Thus, high-quality prediction is not possible in this situation. In this chapter, a novel strategy on Web prediction is suggested using the real-time characteristics of users. Overall, four events have been demonstrated and further compared for finding the most efficient technique of Web prediction having least processing time. The proposed technique requires no Web-log. Mouse movement and its real-time direction are utilized for the prediction of the next probable Web page. Mouse position is tracked as an alternative of using traditional Markov model. Entirely dynamic Web prediction scheme is introduced in the proposed approach due to the fact that Web-log has not been utilized. Minimization of total number of hyperlinks to be selected is the main aim of the proposed approach for accomplishing superior precision in dynamic prediction mode. The proposed approach shows the step-wise build-up of a concrete Web prediction agenda applicable in both personal and public environment. An earlier version of this work has been published in [1]. This version mainly concentrates on social networking. The proposed research also attempts to improve Web prediction technique focusing on the specialized methodologies with the objective of increasing its precision. This approach reduces the users’ perceived latency with no additional cost over the basic mechanism.
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Acknowledgements
The author would like to acknowledge the many helpful suggestions of the participants of The International ACM Conference on Management of Emergent Digital EcoSystems (MEDES 2010), Bangkok, Thailand, on earlier paper based on which this chapter is designed.
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Kundu, A. (2012). Dynamic Web Prediction Using Asynchronous Mouse Activity. In: Abraham, A., Hassanien, AE. (eds) Computational Social Networks. Springer, London. https://doi.org/10.1007/978-1-4471-4048-1_10
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DOI: https://doi.org/10.1007/978-1-4471-4048-1_10
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