Hybrid Recommender System for Ordering Dishes Based on Dish Characteristic and Rating

JOURNAL OF APPLIED INFORMATICS AND COMPUTING

View Publication Info
 
 
Field Value
 
Title Hybrid Recommender System for Ordering Dishes Based on Dish Characteristic and Rating
Sistem Rekomendasi Hybrid untuk Pemesanan Hidangan Berdasarkan Karakteristik dan Rating Hidangan
 
Creator Tommy, Lukas
Novianto, Dian
Japriadi, Yohanes Setiawan
 
Description The method that was often applied in recommender systems was content-based filtering or collaborative filtering which had several drawbacks if applied singly so that its accuracy was not too high. This study intended to solve the drawbacks of both by combining these two methods into a hybrid method. Apriori algorithm was used to provided recommendations based on dishes’s category and price range in customer order history or wishlist. The similarity between dishes was calculated using adjusted-cosine similarity algorithm while customer’s rating for dishes prediction was calculated using weighted sum algorithm. The values generated by these two methods were then averaged for recommendation process. The proposed hybrid recommender system successfully combines content-based with collaborative filtering methods where its precision and recall values when measured by confusion matrix are 80.73% and 76.52%. By considering the characteristics of dishes that have been ordered by customer, the recommender system is able to recommend new dishes or dishes that have not been ordered as long as their characteristics are similar to the dishes the customer has ordered.
 
Publisher Politeknik Negeri Batam
 
Date 2020-12-06
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
 
Format application/pdf
 
Identifier https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/2687
10.30871/jaic.v4i2.2687
 
Source Journal of Applied Informatics and Computing; Vol 4 No 2 (2020): Desember 2020; 137-145
2548-6861
10.30871/jaic.v4i2
 
Language eng
 
Relation https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/2687/1264
 
Rights Copyright (c) 2020 Lukas Tommy, Dian Novianto, Yohanes Setiawan Japriadi
http://creativecommons.org/licenses/by-sa/4.0
 

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us

Twitter

Copyright © 2015-2018 Simon Fraser University Library