@inproceedings{1142d846836c4bc0aab1ecba32089c9b,
title = "K-means Clustering Powered Context Aware Food Recommender System",
abstract = "This paper designed a K-means clustering powered context aware food recommender system (CAFRS). The CAFRS is based on dividing the food data sets into clusters to minimize the error between the user input and available data by computing the distance between the cluster centroid and available food data in the data set of CAFRS. Recommended food has the minimum distance from the K-means clustering (KMC) centroid. Root mean square error (RMSE) and mean absolute error (MAE) are computed to evaluate requirement of the repeat orders to meet energy demand of the user. Proposed CAFRS is effective to recommend a suitable recipe for the user and RMSE and MAE are effective to estimate the requirement of repeat order. Performance of the proposed CAFRS is superior compared to a rule based food recommender system (RBFRS). This study is performed in MATLAB environment.",
keywords = "Context aware food recommender system, Food preference, K-means clustering, Mean absolute error, Root mean square error",
author = "Minakshi Panwar and Ashish Sharma and Mahela, \{Om Prakash\} and Baseem Khan",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023 ; Conference date: 24-11-2023 Through 25-11-2023",
year = "2023",
doi = "10.1109/ICIICS59993.2023.10421677",
language = "English",
series = "International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023",
address = "United States",
}