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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Automated Menu Recommendation System Based on Past Preferences

Author 1: Daniel Simon Sanz
Author 2: Ankur Agrawal

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2014.050711

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 5 Issue 7, 2014.

  • Abstract and Keywords
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Abstract: Data mining plays an important role in ecommerce in today’s world. Time is critical when it comes to shopping as options are unlimited and making a choice can be tedious. This study presents an application of data mining in the form of an Android application that can provide user with automated suggestion based on past preferences. The application helps a person to choose what food they might want to order in a specific restaurant. The application learns user behavior with each order - what they order in each kind of meal and what are the products that they select together. After gathering enough information, the application can suggest the user about the most selected dish in the recent past and since the application started to learn. Applications, such as these, can play a major role in helping make a decision based on past preferences, thereby reducing the user involvement in decision making.

Keywords: data mining; Apriori; Android; restaurant; recommendation system

Daniel Simon Sanz and Ankur Agrawal, “Automated Menu Recommendation System Based on Past Preferences” International Journal of Advanced Computer Science and Applications(IJACSA), 5(7), 2014. http://dx.doi.org/10.14569/IJACSA.2014.050711

@article{Sanz2014,
title = {Automated Menu Recommendation System Based on Past Preferences},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2014.050711},
url = {http://dx.doi.org/10.14569/IJACSA.2014.050711},
year = {2014},
publisher = {The Science and Information Organization},
volume = {5},
number = {7},
author = {Daniel Simon Sanz and Ankur Agrawal}
}


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