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DOI: 10.14569/IJACSA.2022.0131071
PDF

Exponential Decay Function-Based Time-Aware Recommender System for e-Commerce Applications

Author 1: Ayat Yehia Hassan
Author 2: Etimad Fadel
Author 3: Nadine Akkari

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 10, 2022.

  • Abstract and Keywords
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Abstract: Unlike traditional recommendation systems that rely only on the user's preferences, context-aware recommendation systems (CARS) consider the user's contextual information such as (time, weather, and geographical location). These data are used to create more intelligent and effective recommendation systems. Time is one of the most important and influential factors that affect users’ preferences and purchasing behavior. Thus, in this paper, time-aware recommendation systems are investigated using two common methods (Bias and Decay) to incorporate the time parameter with three different recommendation algorithms known as Matrix Factorization, K-Nearest Neighbor (KNN), and Sparse Linear Method (SLIM). The performance study is based on an e-commerce database that includes basic user purchasing actions such as add to cart and buy. Results are compared in terms of precision, recall, and Mean Average Precision (MAP) parameters. Results show that Decay-MF and Decay-SLIM outperform the Bias CAMF and CA-SLIM. On the other hand, Decay-KNN reduced the accuracy of the RS compared to the context-unaware KNN.

Keywords: Time-aware recommender system; context-aware recommender system; matrix factorization; K-Nearest Neighbor (KNN); and Sparse Linear Method (SLIM)

Ayat Yehia Hassan, Etimad Fadel and Nadine Akkari, “Exponential Decay Function-Based Time-Aware Recommender System for e-Commerce Applications” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131071

@article{Hassan2022,
title = {Exponential Decay Function-Based Time-Aware Recommender System for e-Commerce Applications},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131071},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131071},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Ayat Yehia Hassan and Etimad Fadel and Nadine Akkari}
}



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.

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