Nojaded App: Paper Printing and Recycling Mobile App using Recommender System | ||
| The International Undergraduate Research Conference | ||
| Article 6, Volume 5, Issue 5, 2021, Pages 30-40 PDF (1.37 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/iugrc.2021.243407 | ||
| 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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