User profiling for web personalization using multi agent and DBSCAN based approach

  • Authors

    • Sipra Sahoo Siksha ‘O’ Anusandhan University
    • Bikram Kesari Ratha Utkal University
    2018-06-01
    https://doi.org/10.14419/ijet.v7i2.10224
  • Web Personalization System, User's Interests, Dynamic User Profile, Recommendation Systems, Density-Based Spatial Clustering of Applica-Tinos with Noise-User Profiling

  • Abstract

    The user experience is enhanced by the Web Personalization System (WPS), which depends on the User's Interests (UI) and references are stored in the User Profile (UP). The profiles should be able to adapt and reproduce the change of user’s behavior for such system. Existing web page Recommendation Systems (RS) are still limited by several problems, some of which are the problem of recommending web pages to a new user whose browsing history is not available (Cold Start), sparse data structures (Sparsity), and the problem of over-specialization. In this paper, the UI has been tracked and Dynamic User Profiles have been maintained by introducing a method called Density-Based Spa-tial Clustering of Applications with Noise-User Profiling (DBSCAN-UP). The mapping web pages, construct the ontological concepts, which represent the UI, and the interests of users are learned by the reference ontology, which are used to map the visited web pages. The process of storage, management and adaptation of UI is facilitated by multi-agent system. The different user browsing behaviors learning and adapting capability is built in the proposed system and the efficiency of the DBSCAN-UP model is evaluated by the series of experi-ments. The accuracy of the DBSCAN-UP was achieved up to 5% compared to the existing methods.

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  • How to Cite

    Sahoo, S., & Kesari Ratha, B. (2018). User profiling for web personalization using multi agent and DBSCAN based approach. International Journal of Engineering & Technology, 7(2), 849-854. https://doi.org/10.14419/ijet.v7i2.10224

    Received date: 2018-03-17

    Accepted date: 2018-05-18

    Published date: 2018-06-01