A fuzzy preference tree-based recommender system for medical database

  • Authors

    • Pradeepini Gera
    • Vishnu Bhargavi Sabbisetty
    • Tejaswini Devarasetty
    • Madhusri Nukala
    • Navyasri Vittamsetty
    2017-12-21
    https://doi.org/10.14419/ijet.v7i1.1.9712
  • Comprehensive Tree Matching Method, Fuzzy Preference, Fuzzy Techniques, Fuzzy Tree-Established, Recommender System.

  • Nowadays every online site is using personalized recommender systems to suggest a right product for the customer. But existing system has tree structures and have unrequired items in the user preferences. So, it requires high memory and time. To overcome this issue,proposed a new method with increased performance. Firstly, introduced a technique for modeling fuzzy tree-established consumer pref-erences, in which fuzzy set techniques are used to express user choices. A recommendation approach to recommend tree-dependent items is then advanced. The critical path on this study is a comprehensive tree matching method, which can compare two tree-established facts and identify their corresponding components by taking into consideration of all the records on tree structures, weights, and the nodeattributes.The proposed fuzzy preference tree based recommender system is tested using a medical dataset.

  • References

    1. [1] Hua-Ming Wang,GeYu,"Personalized recommendation system K- neighbor algorithm optimization ", ICITEL 2015.

      [2] Vivek Sharma, Sandeep Gonnade, "A Survey on Recommendation System Based on K-Nearest Neighbor Algorithm and Sentiment Analysisâ€, IJIACS ISSN 2347 – 8616, Vol. 4, Issue: 12, 2015.

      [3] Kaustubh Kulkarni, Keshav Wagh , Swapnil Badgujar , Jijnasa Patil " A Study Of Recommender Systems With Hybrid Collaborative Filtering “, IRJET , Vol. 03, Issue: 04 , Apr-2016 .

      [4] Badrul Sarwar, George Karypis , Joseph Konstan , and John Riedl, “ Item-based Collaborative Filtering Recommendation Algorithmsâ€, WWW10, 2001.

      [5] Hui Li, Cun-hua Li, Shu Zhang, “Learning to Recommend Product with the Content of Web Pageâ€, IEEE, 2009. https://doi.org/10.1109/FSKD.2009.704.

      [6] SutheeraPuntheeranurak, PongpanPitakpaisarnsin, “Time-aware Recommender System Using Naïve Bayes Classifier Weighting Techniqueâ€, 2nd International Symposium on Computer, Communication, Control and Automation , 3CA 2013 https://doi.org/10.2991/3ca-13.2013.66.

      [7] Nitin Agarwal, EhteshamHaque, Huan Liu, and Lance Parsons, “Research Paper Recommender Systems: A Subspace Clustering Approachâ€, WAIM 2005, LNCS 3739, pp. 475–491, 2005. c Springer-Verlag Berlin Heidelberg 2005.

      [8] WU Yuan-hong, TAN Xiao-qiu, “A Real-time Recommender System Based on hybrid collaborative filteringâ€, The 5th International Conferenc, 24–27, 2010.

      [9] Megha Jain “Algorithm for research paper recommender systemâ€, International Journal of Information Technology and Knowledge Management July 2012, Vol. 5, No. 2, pp: 443-445

      [10] DaniarAsanov “Algorithms and methods in recommender system ".

      [11] Greg Linden, Brent Smith, and Jeremy York, “Amazon.com Recommendations Item-to-Item Collaborative filteringâ€, Industry report.

      [12] Bela Gipp, JöranBeel, Christian Hentschel, “Scienstein: A Research Paper Recommender Systemâ€.

      [13] Muktakohar, Chhavi Rana “Survey Paper on Recommendation Systemâ€, International Journal of Computer Science and Information Technologies, Vol. 3 , 2012,3460-3462 .

      [14] Stuart E. Middleton, David De Roure, and Nigel R. Shadbolt, “ Ontology-Based Recommender Systemsâ€, Handbook on Ontologies, International Handbooks on Information Systems, Springer-Verlag Berlin Heidelberg 2009. https://doi.org/10.1007/978-3-540-92673-3_35.

  • Downloads

  • How to Cite

    Gera, P., Bhargavi Sabbisetty, V., Devarasetty, T., Nukala, M., & Vittamsetty, N. (2017). A fuzzy preference tree-based recommender system for medical database. International Journal of Engineering & Technology, 7(1.1), 319-321. https://doi.org/10.14419/ijet.v7i1.1.9712