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

  • Abstract

    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.

     

     

  • References

    1. [1] K. Miyahara and M. J. Pazzani, “Collaborative filtering with the simple Bayesian classifier,†in Proceedings of the 6th Pacific Rim International Conference on Artificial Intelligence, pp. 679–689, 2000.

      [2] D. Billsus and M. Pazzani, “Learning collaborative information filters,†in Proceedings of the 15th International Conference on Machine Learning (ICML '98), 1998.

      [3] B. M. Kim and Q. Li, “Probabilistic model estimation for collaborative filtering based on items attributes,†in Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI '04), pp. 185–191, Beijing, China, September 2004.

      [4] B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,†in Proceedings of the 10th International Conference on World Wide Web (WWW '01), pp. 285–295, May 2001.

      [5] K. Miyahara and M. J. Pazzani, “Improvement of collaborative filtering with the simple Bayesian classifier,†Information Processing Society of Japan, vol. 43, no. 11, 2002.

      [6] M Prem Melville,, Vikas Sindhwani, “Recommender System†Encyclopedia in Machine Learningâ€,, Springer Science and bussiness media, pp. 829–838,2011.

      [7] Balabanovic, M. Andashoham,. FAB: Content-based collaborative recommendation. Commun. ACM 40, 3 (Mar.) y. 1997.

      [8] Basu, c., hirsh, h., and cohen, Recommendation as classification: Using social and content-based information in recommendation. In Proceedings of the 1998 Workshop on Recommender Systems. AAAI Press, Reston, Va. 11–15, 1998

      [9] BEEFERMAN, D. AND BERGER, “Agglomerative clustering of a search engine query logâ€, In Proceedings of ACM SIGKDD International Conference. ACM, New York, 407–415, 2000

      [10] KITTS, B., FREED, D., AND VRIEZE “Cross-sell: A fast promotion-tunable customer–item recommendation method based on conditional independent probabilitiesâ€, In Proceedings of ACM SIGKDD International Conference. , ACM, New York, 437–446, 2000

      [11] Lewis. D. Evaluating Text Categorization. In Proceedings of the Speech and Natural Language Workshop. Asilomar, CA., 1991

      [12] L.M. de Campos, J.M. Fernández-Luna, J.F. Huete, M.A. Rueda-Morales, Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks.

      [13] Balabanovic, M.; and Shoham¸ContentBased, Collaborative Recommendation. Communications of the ACM Vol. 40, No. 3. March, 1997.

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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

    Received date: 2018-09-24

    Accepted date: 2018-09-24

    Published date: 2018-09-22