Web navigation prediction using Markov-based models: an experimental study. (2016) - Jindal, Honey; Sardana, Neetu
Navigation prediction on web has gained a lot of importance due to its rapid growth. Web navigation prediction (WNP) deals with discovering the future pages for a particular website that users/the user may visit based on previously accessed web pages. WNP can be applied effectively in many applications such as web related search, latency reduction, improving website design, location prediction and personalised systems. Markov models and its variations are widely used to represent and analyse the navigational data on web. It can also used to predict user navigation behaviour. Diverse Markov model varies in terms of state-space complexity, prediction accuracy, model accuracy, coverage and failure cases. Research is still in progress for determining the suitability of Markov models for various web applications. Therefore, this paper presents a comparison of existing Markov-based models and its merit lies in the non-trivial conclusions derived by the experiments. Comparison is done in terms of state-space complexity, failure cases, coverage, prediction accuracy and model accuracy.