CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

TFR: A Tourist Food Recommender System based on Collaborative Filtering

by Neda Rajabpour, Amirmahdi Mohammadighavam, Ali Naserasadi, Majid Estilayee
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 11
Year of Publication: 2018
Authors: Neda Rajabpour, Amirmahdi Mohammadighavam, Ali Naserasadi, Majid Estilayee
10.5120/ijca2018917695

Neda Rajabpour, Amirmahdi Mohammadighavam, Ali Naserasadi, Majid Estilayee . TFR: A Tourist Food Recommender System based on Collaborative Filtering. International Journal of Computer Applications. 181, 11 ( Aug 2018), 30-39. DOI=10.5120/ijca2018917695

@article{ 10.5120/ijca2018917695,
author = { Neda Rajabpour, Amirmahdi Mohammadighavam, Ali Naserasadi, Majid Estilayee },
title = { TFR: A Tourist Food Recommender System based on Collaborative Filtering },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2018 },
volume = { 181 },
number = { 11 },
month = { Aug },
year = { 2018 },
issn = { 0975-8887 },
pages = { 30-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number11/29818-2018917695/ },
doi = { 10.5120/ijca2018917695 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:05:43.925330+05:30
%A Neda Rajabpour
%A Amirmahdi Mohammadighavam
%A Ali Naserasadi
%A Majid Estilayee
%T TFR: A Tourist Food Recommender System based on Collaborative Filtering
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 11
%P 30-39
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A Nowadays using recommender systems (systems that help you to choose something) is so widespread that we can say their usage is one of the most vital necessities of human being. These systems have been made to help the users to choose the best alternative on the basis of their preferences. On the other hand, in the tourism industry, as one of the most profit-making industries, most tourists are not familiar with the foods of the countries that they have travelled to, so it is possible that they choose a kind of food that they don’t like or it is dangerous for their health because of possible disease that they suffer from. In this paper, a system is proposed for solving this problem of tourism industry, called TFR. The purpose of this system is to recommend foods to tourists according to their preferences. Moreover, TFR is able to recommend a special food to a tourist in case he/she has a special diet. To evaluate the presented system which is based on collaborative filtering, it has been used by some real users. The results show that the accuracy of TFR is 86.3%, indicating the suitable efficiency of the system.

References
  1. Efraim Turban, Jae Lee, David King, and H. Michael Chung. Electronic Commerce: A Managerial Perspective. Prentice Hall, 2000.
  2. Maria S.Pera,Y-Kai .N (2013). A group recommender for movies based on content similarity and popularity .information processing and management , 673-687.
  3. Ingrid A.Christensen, S. S. (2011). entertainment recommender system for group of user. expert systems with applications , 14127-14135.
  4. Joseph A. Konstan, Bradley N. Miller, David Maltz, Jonathan.L. Herlocker, Lee R. Gordon, and John Riedl 1997.GroupLens: Applying collaborative filtering to Usenet news.ommunications of the ACM, 40(3): pp 77-87.
  5. M. Tavakolifard, J. Gulla, K. Almeroth, J. Ingvaldesn,G. Nygreen, and E. Berg. Tailored news in the palm of your hand: a multi-perspective transparent approachto news recommendation. In Int. Conf. on WorldWide Web, pages 305-308, 2013
  6. konstantinoschristidis,gregorisMentzas. (2013). a topic-based recommender system for electronic marketplace platforms. expert systems with applications , 4370-4379.
  7. José M. Noguera, Manuel J. Barranco, Rafael J. Segura, Luis Martnez. (2012). A mobile 3D-GIS hybrid recommender system for tourism. Information sciences, pages 37-52.
  8. Wan-Shiou Yang, San-Yih Hwang (2013), iTravel: A recommender system in mobile peer-to-peer environment. The Journal of Systems and Software, 12-20.
  9. Chieh-Yuan Tsai, Shang-Hsuan Chung, (2012) A personalized route recommendation service for theme parks using RFID, Decision Support Systems, 514-527.
  10. Duen-YianYeh, Ching-Hsue Cheng, (2015), Recommendation system for popular tourist attractions in Taiwan, Tourism Management, 164-176.
  11. http://en.wikipedia.org/wiki/World_Tourism_rankings
  12. M. Svensson, K. hook, R. Coster, 2005, Designing and Evaluating Kalas: A Social Navigation System for Food Recipes, ACM Transactions on Computer-Human Interaction, Vol. 12, No. 3, Pages 374–400.
  13. M. Svensson, K. Höök, J. Laaksolahti, A. Waern, Social Navigation of Food Recipes, 2001, Proceeding of the SIGCHI, ACM
  14. Jong-Hun Kim, Jung-Hyun Lee, Jee-Song Park, Young-Ho Lee, Kee-Wook Rim (2009) Design of Diet Recommendation System for Healthcare Service Based on User Information International Conference on Computer Sciences and Convergence Information Technology. IEEE, pp. 516-518.
  15. Xiaocheng Li, Xinliu, Zengjie Zhang, Yongming Xia, SongrongQian (2010) Design of Healthy Eating System based on Web Data Mining, WASE International Conference on Information Engineering. IEEE, 346-349
  16. Giuseppe Di Fabbrizio, Narendra Gupta, SvevaBesana, Premkumar Mani, (2010), Have2eat: a Restaurant Finder with Review Summarization for Mobile Phones, pp 17-20
  17. J. F. McCarthy, 2002, Pocket restaurant finder: a situated recommender system for groups, Technology Labs 161 North Clark Street, Chicago, IL 60601 USA.
  18. P. Resnick, HR. Varian, 1997, a survey on recommender systems‏, Communications of the ACM, vol. 40, pp. 56-59.
  19. M. Rashid, I. Albert, Dan Cosley, Shyong K. Lam, Sean M. Mcnee, Joseph A. Konstan, J. Riedl, 2002, Getting to Know you: Learning new user preferences in recommender systems, ACM.
  20. S. H. Ha, 2006, Digital content recommender on the internet, Intelligent Systems, IEEE, vol. 21, No. 2, pp. 70–77.
  21. D. Li, Q. Lv, H. Xia, L. Shang, 2011, Lupistis: A privacy-preserving content recommender system for online social communities‏, Web Intelligence and Intelligent Agent Technology (WI-IAT), Vol.1. pp. 79-86.
  22. J. L. Herlocker, J. Konstan, L. G. Terveen, J. T. Ridel, 2004, Evaluating Collaborative Filtering Recommender Systems, ACM Transactions on Information Systems, Vol. 22, No. 1, Pages 5–53.
  23. Ji-Chun Quan, Sung-Bae Cho, A Hybrid Recommender System Based on AHP That Awares Contexts with Bayesian Networks for Smart TV, Hybrid Artificial Intelligence Systems, Volume 8480, 2014, pp 527-536
  24. A. I. Schein, A. Popescul, L. H. Ungar, and D. M.Pennock. Generative models for cold-start recommendations. In Proceedings of the 2001 SIGIR Workshop on Recommender Systems, 2001.
  25. SCHEIN, A.L. POPESCUL, A. UNGAR, L.H. 2002. Methods and Metrics for Cold-Start Recommendations. In SIGIR’02, Tampere, Finland
  26. L. Safoury, A. Salah, 2013, Exploiting User Demographic Attributes for Solving Cold-Start Problem in Recommender System, Software Engineering, Vol. 1, pp. 1-22.
  27. Dong-Her Shih, David C.Yen, Ho-Cheng Lin, Ming-Hung Shih. 2011, An implementation and evaluation of recommender systems for traveling abroad, Expert Systems With Applications, Vol.38, pp. 15344-15355.
  28. Garsia, L. Sebastia, E. Onaindia, 2011, On the design of individual and group recommender systems for tourism. expert systems with applications, vol. 38. Issue 6, pp.7683-7692.
  29. L. Martinez, R.M. Rodrigues, M. Espinilla, 2009, REJA: A Georeferenced Hybrid recommender system for restaurants. IEEE, International conference on Web intelligence and intelligent agent technology.
  30. ZiHanZhuo, WeiMingZhai, Xin Li, LingLing Liu, JinTian Tang, 2014, Local adaptive segmentation algorithm for 3-D medical image based on robust feature statistics, Science China Information Sciences, Volume 57, Issue 10, pp 1-12
  31. Gunawan,M.A.Sulaiman, F. ; Putra, T.A. ; Hasudungan, C.D. ; Sari, R.F. 2014, Object-following robot using adaptive cruise control algorithm with IOIO, Intelligent Green Building and Smart Grid (IGBSG), 2014 International Conference. IEEE. Pages 1-5.
Index Terms

Computer Science
Information Sciences

Keywords

Food Recommender System Tourist Recommender System Collaborative Filtering Content-Based Filtering

Powered by PhDFocusTM