Nojaded App: Paper Printing and Recycling Mobile App using Recommender System

Gomaa, Ramy; Ghoweil, Noha; Ahmed, Mostafa; Ayman, Mostafa; Mostafa, Noran; Magdy, Ahmed; El-Bendary, Nashwa; Hassan, Ammar M.

doi:10.21608/iugrc.2021.243407

	Nojaded App: Paper Printing and Recycling Mobile App using Recommender System
The International Undergraduate Research Conference
Article 6, Volume 5, Issue 5, 2021, Page 30-40 PDF (1.37 MB)
Document Type: Original Article
DOI: 10.21608/iugrc.2021.243407
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Authors
Ramy Gomaa¹; Noha Ghoweil¹; Mostafa Ahmed¹; Mostafa Ayman¹; Noran Mostafa¹; Ahmed Magdy¹; Nashwa El-Bendary²; Ammar M. Hassan³
¹Arab Academy for Science and Technology, Egypt.
²Dean of the Computer Science Department, Arab Academy for Science and Technology, Egypt.
³Head of the Computer Science Department, Arab Academy for Science and Technology, Egypt.
Abstract
Paper Recycling has always been a major issue and according to the Food and Agricultural Organization of the United Nations (FAO), the total global paper use is still steadily increasing. We argue that, among many factors, Homeowners' failure to divide up waste is one of the causes. The objective of this research is to design the Nojaded app, a mobile application that uses recommendation techniques to ease the paper recycling operations by facilitating communication between the recycling organizations and the paper donors and also facilitating the paper printing operations for users by creating a platform for which they can easily use to print or purchase papers and receive them from their house step. The recommendation engine in this research uses a combination of Collaborative Filtering (CF) and Content-Based Filtering. CF is used to perform data filtering based on the similarities of user characteristics, which will help us identify patterns that choose the appropriate parameters dealing with users, whether it is for paper donation or paper purchasing. Content-Based Filtering is used to enhance the personalization experience of the user in the paper printing side of the app. The used datasets have features that benefit the recommender system and help build a model for the user. These features are obtained from the printing centers and the users. Each operation has a rating. The filtering methods are measured for accuracy using Mean Absolute Error (MAE) with a most significant MAE of 2.27.
Keywords
Recommender System; Collaborative Filtering; Content-Based Filtering; Paper Recycling; Paper Printing


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