A fuzzy preference tree-based recommender system for medical database

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • 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 nodeattributes.The proposed fuzzy preference tree based recommender system is tested using a medical dataset.


  • Keywords


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

  • References


      [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 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 Filtering “, 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] SutheeraPuntheeranurak, PongpanPitakpaisarnsin, “Time-aware Recommender System Using Naïve Bayes Classifier Weighting Technique”, 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 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 System 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] DaniarAsanov “Algorithms and methods in recommender system ".

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

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

      [13] Muktakohar, Chhavi Rana “Survey Paper on Recommendation System”, 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 Systems”, Handbook on Ontologies, International Handbooks on Information Systems, Springer-Verlag Berlin Heidelberg 2009. https://doi.org/10.1007/978-3-540-92673-3_35.


 

View

Download

Article ID: 9712
 
DOI: 10.14419/ijet.v7i1.1.9712




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.