|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
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
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.