Prediction of User's Web-Browsing Behavior: Application of Markov Model

Abstract

Web prediction is a classification problem in which we attempt to predict the next set of Web pages that a user may visit based on the knowledge of the previously visited pages. Predicting user's behavior while serving the Internet can be applied effectively in various critical applications. Such application has traditional tradeoffs between modeling complexity and prediction accuracy. In this paper, we analyze and study Markov model and all- Kth Markov model in Web prediction. We propose a new modified Markov model to alleviate the issue of scalability in the number of paths. In addition, we present a new two-tier prediction framework that creates an example classifier EC, based on the training examples and the generated classifiers. We show that such framework can improve the prediction time without compromising prediction accuracy. We have used standard benchmark data sets to analyze, compare, and demonstrate the effectiveness of our techniques using variations of Markov models and association rule mining. Our experiments show the effectiveness of our modified Markov model in reducing the number of paths without compromising accuracy. Additionally, the results support our analysis conclusions that accuracy improves with higher orders of all- Kth model.

A Hybrid Approach of Association Rule & Hidden Makov Model to Improve Efficiency Medical Text Classification

“…speech [35], handwriting [36] and bioinformatics [41], [47]. the model has been extended to the text-related task such as information retrieval [31] information extraction [13], [26] text summarization [15] text categorization [6], [12], [42], [2] also the model has been turned to the hybrid and novel model [41], [22], [21] .In [31], the research use HMM in an information retrieval model. Given a set of documents and a query Q, the system searches a document D relevant to the query Q.…”

“…The process shows the semantic character in different documents to make the text categorization process more stable and accurate.by [6] proposed novel two tier prediction framework and present probabilistic model such as Markov model and association rule mining. The models gives better prediction accuracy without compromising prediction time but suffers to scale on larger datasets.…”

Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study

“…Other, more recent works regarding the application of such models to the same domain include those of Maheswara-Rao & Valli-Kumari [19] and Madhuri et al [5], where different variable kth-order Markov models are used in order to face the problem of the big amount of data. Moreover, in the works of Awad & Khalil [3] and Vishwakarma et al [23], socalled all-kth models are used for similar purposes. Additionally, some works can be found in the fields of social networks, such as those by Lerman & Hogg [17] and Hogg et al [12] where Markov models are used to predict the behavior of users in Digg and Twitter respectively; e-commerce, such as works by Rendle et al [20] and Wu et al [25] where Markov chains are used for recommending items to users (the second one using distributed processing to accommodate the presence of big data); education, such as the work of Marques & Belo [18] where Markov chains are applied for discovering user profiles in e-learning sites in order to customize the learning experience; or even in distributed systems, such as the works from Bolivar et al where matchmakers based on hidden Markov models are proposed predict the availability of grid resources with the ultimate purpose of decentralized grid scheduling [6,7].…”

Advertisement Mining using Hidden Markov Model

“…The main objective of these prediction models is to attain more prediction accuracy. In Hidden Markov model the term hidden refers to sequence of state through which the model get ahead of [1]. In general, Markov model has functionality to predict future action based on the results of previous actions.…”