Implementation of Recommender System for Web pages

  • Authors

    • Anuradha G
    2018-09-22
    https://doi.org/10.14419/ijet.v7i4.5.20188
  • Collaborative filtering, Content filtering, Sparsity, The cold-start problem, Fraud

  • In Web based applications, Recommender systems have become fundamental information access system. They effectively prune large information spaces which provide appropriate decision making and suggestions, so that the users are directed towards those web pages that best meet their needs, preferences and interests. In web-based context, this can be achieved by basic rough set model and collaborative filtering techniques in decision making of the web pages. The recommender system can be implemented based on two types of techniques which are content based filtering and collaborative filtering. Content based filtering constructs a recommendation on basis of user’s behavior (historical browsing information) and collaborative filtering uses group knowledge to form a recommendation based on like users. Sparsity, the cold-start problem, fraud are main challenges in recommender system.

    This paper proposes recommendations to the user that varies from one user to another user which is based on the user's profile. (For example, for a search with same keyword a student will be suggested differently compared to a scholar or a teacher).

    The abstract should state the purpose, approach, results and conclusions of the work.  The author should assume that the reader has some knowledge of the subject but has not read the paper. Thus, the abstract should be intelligible and complete in it-self (no numerical references); it should not cite figures, tables, or sections of the paper. The abstract should be written using third person instead of first person.

     

     

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  • How to Cite

    G, A. (2018). Implementation of Recommender System for Web pages. International Journal of Engineering & Technology, 7(4.5), 386-388. https://doi.org/10.14419/ijet.v7i4.5.20188