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General Information
    • ISSN: 2010-3700
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Engineering & Technology Digital Library, Google Scholar, Crossref, ProQuest, Electronic Journals Library, DOAJ and EI (INSPEC, IET).
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol.4(4): 307-312 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.429

Recommendations of Personal Web Pages Based on User Navigational Patterns

Yin-Fu Huang and Jia-Tang Jhang
Abstract—In this paper, we propose a web recommendation system where user navigational patterns can be extracted from web logs. First, the recommendation system discovers user concepts from web logs step-by-step, and then extracts the navigation patterns among these concepts. These navigational patterns are then used to generate recommendation web pages by matching the navigation behavior of a user personal knowledge base. The pages in a recommendation list are ranked according to their hub scores which are computed based on page connectivity information. The experimental results show that the web pages recommended by our system are of better quality and acceptable for humans from various domains, based on human evaluators ranking as well as quality-value-based performance measures.

Index Terms—Information retrieval, recommendation system, web mining, web search.

Yin-Fu Huang is with National Yunlin University of Science and Technology, Yunlin, Taiwan 640 (e-mail: huangyf@yuntech.edu.tw).
Jia-Tang Jhang is with Tornado Technologies Co., Ltd, Taipei, Taiwan 106 (e-mail: 9917711@yuntech.edu.tw).

[PDF]

Cite: Yin-Fu Huang and Jia-Tang Jhang, "Recommendations of Personal Web Pages Based on User Navigational Patterns," International Journal of Machine Learning and Computing vol.4, no. 4, pp. 307-312, 2014.

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