A possiblistic clustering based biased Bayesian relevance feedback model for web usage recommendation

  • Authors

    • Sumanth Venugopal RAJAREDDY INSTITUTE OF TECHNOLOGY
    • Guruprasad Nagraj NEW HORIZON COLLEGE OF ENGINEERING
    2018-12-17
    https://doi.org/10.14419/ijet.v7i4.16224
  • Possibility Fuzzy C-Means, Recommendation System, Relevance Feedback Biased Bayesian, Web Usage Mining.

  • Abstract

    World Wide Web (WWW) consists of a huge amount of web pages and links, provides massive information to the internet users. The de-velopments of websites have become challenging thing and size of web contents is more abundant. The web usage mining technique is em-ployed in web server log for extracting the user information. Presently, the Web Recommendation System (RS) is rapidly developing and major objective is generating the customized data for the end users. The RS is the platform that make personalized recommendations for a particular user by predicting the ratings for different items. In this paper, an efficient web RS that consists of two methods such as Possibil-istic Fuzzy C-Means (PFCM) and Relevance Feedback Biased Bayesian Network (RFBBN) methods are proposed. The PFCM algorithm clusters the similar web page users. In these clusters RFBBN model extract the relevant information and predict the relevant web pages. The proposed method reduces the loss of the end users. The experimental analysis demonstrated that the PFCM-RFBBN approach delivered the high priority of web pages and also recommended the related web pages. Finally, the experimental outcome showed that the proposed ap-proach improved accuracy in web page recommendation up to 31% compared to the existing methods.

     

     

     

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  • How to Cite

    Venugopal, S., & Nagraj, G. (2018). A possiblistic clustering based biased Bayesian relevance feedback model for web usage recommendation. International Journal of Engineering & Technology, 7(4), 4024-4029. https://doi.org/10.14419/ijet.v7i4.16224

    Received date: 2018-07-25

    Accepted date: 2018-12-01

    Published date: 2018-12-17