Interdisciplinary Journal of Information, Knowledge, and Management 影响因子-刊鹿论文编译

Improving Webpage Access Predictions Based on Sequence Prediction and PageRank Algorithm

Abstract:

Aim/Purpose In this article, we provide a better solution to Webpage access prediction. In particularly, our core proposed approach is to increase accuracy and efficiency by reducing the sequence space with integration of PageRank into CPT+. Background The problem of predicting the next page on a web site has become significant because of the non-stop growth of Internet in terms of the volume of contents and the mass of users. The webpage prediction is complex because we should consider multiple kinds of information such as the webpage name, the contents of the webpage, the user profile, the time between webpage visits, differences among users, and the time spent on a page or on each part of the page. Therefore, webpage access prediction draws substantial effort of the web mining research community in order to obtain valuable information and improve user experience as well. Methodology CPT+ is a complex prediction algorithm that dramatically offers more accurate predictions than other state-of-the-art models. The integration of the importance of every particular page on a website (i.e., the PageRank) regarding to its associations with other pages into CPT+ model can improve the performance of the existing model. Contribution In this paper, we propose an approach to reduce prediction space while improving accuracy through combining CPT+ and PageRank algorithms. Experimental results on several real datasets indicate the space reduced by up to between 15% Improving Webpage access Predictions 28 and 30%. As a result, the run-time is quicker. Furthermore, the prediction accuracy is improved. It is convenient that researchers go on using CPT+ to predict Webpage access. Findings Our experimental results indicate that PageRank algorithm is a good solution to improve CPT+ prediction. An amount of though approximately 15 % to 30% of redundant data is removed from datasets while improving the accuracy. Recommendations for Practitioners The result of the article could be used in developing relevant applications such as Webpage and product recommendation systems. Recommendations for Researchers The paper provides a prediction model that integrates CPT+ and PageRank algorithms to tackle the problem of complexity and accuracy. The model has been experimented against several real datasets in order to show its performance. Impact on Society Given an improving model to predict Webpage access using in several fields such as e-learning, product recommendation, link prediction, and user behavior prediction, the society can enjoy a better experience and more efficient environment while surfing the Web. Future Research We intend to further improve the accuracy of webpage access prediction by using the combination of CPT+ and other algorithms.

Author Listing： Da Thon Nguyen;Hanh T Tan;Duy Hoang Pham

Volume： 14

Pages： 027-044

DOI： 10.28945/4176

Language： English

Journal： Interdisciplinary Journal of Information, Knowledge, and Management

Interdisciplinary Journal of Information, Knowledge, and Management

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ISSN：1555-1229

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收录数据库：Scopus收录

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出版社：Informing Science Institute

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偏重研究方向	Computer Science-Computer Science (all)
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