A fuzzy preference tree-based recommender system for medical database

  • Authors

    • Pradeepini Gera
    • Vishnu Bhargavi Sabbisetty
    • Tejaswini Devarasetty
    • Madhusri Nukala
    • Navyasri Vittamsetty
    https://doi.org/10.14419/ijet.v7i1.1.9712

    Received date: February 25, 2018

    Accepted date: February 25, 2018

    Published date: December 21, 2017

  • Comprehensive Tree Matching Method, Fuzzy Preference, Fuzzy Techniques, Fuzzy Tree-Established, Recommender System.

  • Abstract

    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 node attributes. The proposed fuzzy preference tree based recommender system is tested using a medical dataset.

  • References

    1. Hua-Ming Wang,Ge Yu,"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 Analysis”, 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 Fil-tering “, IRJET , Vol. 03, Issue: 04 , Apr-2016 .
    4. Badrul Sarwar, George Karypis , Joseph Konstan , and John Riedl, “ Item-based Collaborative Filtering Recommendation Algorithms”, WWW10, 2001.
    5. Hui Li, Cun-hua Li, Shu Zhang, “Learning to Recommend Product with the Content of Web Page”, IEEE, 2009. https://doi.org/10.1109/FSKD.2009.704.
    6. Sutheera Puntheeranurak, Pongpan Pitakpaisarnsin, “Time-aware Recommender System Using Naïve Bayes Classifier Weighting Technique”, 2nd International Symposium on Computer, Communi-cation, Control and Automation , 3CA 2013 https://doi.org/10.2991/3ca-13.2013.66.
    7. Nitin Agarwal, Ehtesham Haque, Huan Liu, and Lance Parsons, “Research Paper Recommender Systems: A Subspace Clustering Approach”, 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 Sys-tem Based on hybrid collaborative filtering”, The 5th International Conferenc, 24–27, 2010.
    9. Megha Jain “Algorithm for research paper recommender system”, International Journal of Information Technology and Knowledge Management July 2012, Vol. 5, No. 2, pp: 443-445
    10. Daniar Asanov “Algorithms and methods in recommender system ".
    11. Greg Linden, Brent Smith, and Jeremy York, “Amazon.com Rec-ommendations Item-to-Item Collaborative filtering”, Industry report.
    12. Bela Gipp, Jöran Beel, Christian Hentschel, “Scienstein: A Research Paper Recommender System”.
    13. Mukta kohar, Chhavi Rana “Survey Paper on Recommendation Sys-tem”, International Journal of Computer Science and Information Technologies, Vol. 3 , 2012,3460-3462 .
    14. Stuart E. Middleton, David De Roure, and Nigel R. Shadbolt, “ On-tology-Based Recommender Systems”, Handbook on Ontologies, In-ternational 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 and Technology, 7(1.1), 319-321. https://doi.org/10.14419/ijet.v7i1.1.9712