CFP last date
20 January 2025
Reseach Article

Personalized Recommendation System for Medical Assistance using Hybrid Filtering

by Archana B. Salunke, Smita L. Kasar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 128 - Number 9
Year of Publication: 2015
Authors: Archana B. Salunke, Smita L. Kasar
10.5120/ijca2015906626

Archana B. Salunke, Smita L. Kasar . Personalized Recommendation System for Medical Assistance using Hybrid Filtering. International Journal of Computer Applications. 128, 9 ( October 2015), 6-10. DOI=10.5120/ijca2015906626

@article{ 10.5120/ijca2015906626,
author = { Archana B. Salunke, Smita L. Kasar },
title = { Personalized Recommendation System for Medical Assistance using Hybrid Filtering },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 128 },
number = { 9 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 6-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume128/number9/22899-2015906626/ },
doi = { 10.5120/ijca2015906626 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:21:10.242923+05:30
%A Archana B. Salunke
%A Smita L. Kasar
%T Personalized Recommendation System for Medical Assistance using Hybrid Filtering
%J International Journal of Computer Applications
%@ 0975-8887
%V 128
%N 9
%P 6-10
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recommender systems assist the consumers of service oriented environment to find out and select the most suitable services from a large number of available ones. Proposed paper is based on Personalized Recommendation System for medical assistance using keyword extraction. User can search doctor’s profiles or hospital names according to doctor and hospital attributes. Natural Language Processing (NLP) is used to process user’s ratings and reviews to compute system ratings. Depending on users rating and reviews, profiles are recommended. Medical-based Personalized Recommendation System computes similarity between given and collected attribute by using top-k query which is used to recommend each doctor profile and hospital name for each attribute in information retrieval. Personalized Recommendation system for medical assistance yields 0.06 satisfactions and 0.02 accuracy.

References
  1. Bamshad Mobasher, Honghua Dai, Tao Luo, Yuqing Sun and Jiang Zhu, "Integrating Web Usage and Content Mining for More Effective Personalization, " Electronic Commerce and Web Technologies LCNS, vo.1875, pp.165-176, 2000.
  2. Ibrahim Cingil, Asuman Dogac, Ayca Azgin, "A broader approach to personalization," Communications of the ACM, vo.43, Issue 8, pp. 136-141, 2000.
  3. 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.
  4. Gediminas Adomavicius, Alexander Tuzhilin, "User Profiling in Personalization Applications through Rule Discovery and Validation, " ACM, pp.377-381, 1999.
  5. Bamshad Mobasher, Robert Cooley, Jaideep Srivastava, "Automatic personalization based on Web usage mining", Communications of the ACM, vo.43, Issue 8, pp.142-151, 2000.
  6. Joseph Kramer, Sunil Noronha, John Vergo, "A User-centered design approach to personalization" Communications of the ACM, vo.43, Issue 8, pp.44-48, 2000.
  7. Marco Gori, Augusto Pucci, "Research Paper Recommender Systems: A Random-Walk Based Approach, " Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, 2006.
  8. Masashi Shimbo, Takahiko Ito, Yuji Matsumoto "Evaluation of Kernel-based Link Analysis Measures on Research Paper Recommendation, " JCDL '07 Proceedings of the 7th ACM/IEEE-CSjoint conference, 2007.
  9. Bela Gipp, Joran Beel, and Christian Hentschel, "Scienstein: A Research Paper Recommender System, " In Proceedings of the International Conference on Emerging Trends in Computing (iCETiC'09), pp. 309-315,2009.
  10. Andre Vellino, "Recommending Journal Articles with PageRank Ratings, " Recommender Systems 2009.
  11. A. Naak, H. Hage, and E. A(meur, "A Multi-criteria Collaborative Filtering Approach for Research Paper Recommendation in Papyres, " MCETECH 2009, LNBIP 26, pp. 25-39, 2009.
  12. Zhiping Zhang, Linna Li, "A Research Paper Recommender System based on Spreading Activation Model, " IEEE, 2010 2nd International Conference on Information Science and Engineering (ICISE), pp.928-931, 2010.
  13. Worasit Choochaiwattana, "Usage of Tagging for Research Paper Recommendation" 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), vo.2, pp.439-442, 2010.
  14. Chenguang Pan, Wenxin Li, "Research Paper Recommendation with Topic Analysis" 2010 International Conference On Computer Design And Appliations (ICCDA 2010), vo.4, pp.264-268, 2010.
  15. Pijitra Jomsri, Siripun Sanguansintukul, Worasit Choochaiwattana, "A Framework for Tag-Based Research Paper Recommender System: An IR Approach, " 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops, 2010.
  16. Kazunari Sugiyama, Min-Yen Kan, "Scholarly Paper Recommendation via User's Recent Research Interests, " JCDL'10 Proceedings of the 10th annual joint conference on Digital libraries, 2010.
  17. Cristiano Nascimento, Alberto H. F. Laender, Marcos Andre Gonc;:alves, Altigran S. da Silva, "A Source Independent Framework for Research Paper Recommendation, " JCDL '11, 2011.
  18. Kiyoko Uchiyama, Akiko Aizawa, Hidetsugu Nanba, Takeshi Sagara, "OSUSUME: Cross-lingual Recommender System for Research Papers, " CaRR 2011, Proceedings of the 2011 Workshop on Context awareness in Retrieval and Recommendation, 201l.
  19. Felice Ferrara, Nirmala Pudota, and Carlo Tasso, "A Keyphrase-Based Paper Recommender System" IRCDL 2011, CCIS 249, pp. 14-25, 2011.
  20. Manabu Ohta, Toshihiro Hachiki, Atsuhiro Takasu, "Related Paper Recommendation to Support Online Browsing of Research Papers, " IEEE , 2011 Fourth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT), pp.130-136 , 2011.
  21. Jianhua Feng, Guoliang Li, and Jianyong Wang, “Finding Top-k Answers in Keyword Search over Relational Databases Using Tuple Units”, Ieee Transactions On Knowledge And Data Engineering, Vol. 23, No. 12, December 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Recommendation System Personalization Profile Natural Language Processing (NLP) XML Top-k query.