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An Empirical Analysis of Collaborative Filtering Algorithms for Building a Food Recommender System

  • Ashique Mohaimin Ornab
  • Sakia ChowdhuryEmail author
  • Seevieta Biswas Toa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 847)

Abstract

Recommender system has been playing a great role in almost every sectors starting from online shopping Web sites to online movie sites and social networking sites. However, the use of recommendation engine has been very little in the food sector. Sometimes people become tired of having the same kind of meals everyday because of several reasons. Some people need to consume fixed food due to their illness; others consume same meals everyday to stay healthy despite having any diseases. In this paper, we have first discussed two collaborative filtering algorithms that can be used to build a food recommender system for the people who have been leading a monotonous food consumption lifestyle and are bored of having the same kind of meals every day. After that, we have analyzed the two approaches of building a food recommender system and finally concluded that the model-based approach is more reliable than the memory-based approach.

Keywords

Recommender system Collaborative filtering Cosine similarities Alternating least squares (ALS) 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ashique Mohaimin Ornab
    • 1
  • Sakia Chowdhury
    • 1
    Email author
  • Seevieta Biswas Toa
    • 1
  1. 1.Department of Computer Science and EngineeringBRAC UniversityDhakaBangladesh

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