# Recommendation System for a Delivery Food Application Based on Number of Orders
## 6 Conclusions  
This document proposed a recommendation system for a delivery food application based on orders. Our system uses the sales transactions automatically stored by the application, and no questionnaires or ratings are needed from customers. The methodology used for the recommendation is a nearest-neighbor technique that is based on the percentage of orders. The recommendations are based on the preferred restaurants. We defined a preferred restaurant as one with at least 10% of the clients' orders. In our experiments, we used actual data from a local delivery food application with 187 restaurants and 100 customers in Aguascalientes, Mexico. Clients have, on average, only 2.6 preferred restaurants, and 24% of the time, our system can identify one of those preferred restaurants from a list of 7.7 recommended ones (from 187 available). These numbers confirmed to us that our system has a satisfactory performance.  
Previous proposals from the literature used ratings and reviews as training data. However, only some clients proportionate that information after their purchases. This makes the implementation of recommendation systems in real delivery food applications difficult. This research's main contribution is using the number of orders as input for the recommendation system. These data are always stored and available in the delivery food  
Figure 9: Results of matrix factorization according to the values of the parameters _pmtin_ (\(x\)-axis). Vertical lines represent the standard deviation. (**a**) Percentage of correct recommendations (\(y\)-axis) on a scale from 0 to 1. (**b**) The average number of recommended restaurants (\(y\)-axis).  
applications. Therefore, our methodology enables the implementation of a restaurant-recommendation system in real-life scenarios.  
The research of new restaurant-recommendation systems is very important. Many consumers tend only to order in restaurants they know because they fear disappointment and do not explore other options. Therefore, this recommendation system is vital for consumers and restaurants since it can give an excellent suggestion on where to order next with high accuracy based only on clients' previous orders. The recommendations will satisfy the clients, and restaurants can increase their sales.  
The future scope of this research can involve the implementation of this methodology in other areas. For example, recommendations for online sales or bookstores. Likewise, the system could be evaluated in a larger-scale real-world study to further validate its effectiveness in practice.  
Conceptualization, C.N.S. and J.D.-S.; methodology, C.N.S. and J.D.-S.; software, C.N.S. and M.G.; validation, C.N.S. and A.A.; formal analysis, C.N.S. and M.G.; investigation, C.N.S. and J.D.-S.; resources, A.A.; data curation, A.A.; writing--original draft preparation, C.N.S.; writing--review and editing, C.N.S. and J.D.-S.; visualization, C.N.S.; supervision, J.D.-S. and M.G.; project administration, J.D.-S.; funding acquisition, J.D.-S. and A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universidad Panamericana through the grant \"Fomento a la Investigacion UP 2019\", under Project Code UP-CI-2019-DNA-AGS.  
Not applicable.  
The data presented in this study are openly available in our GitHub repository: [[https://github.com/[PERSON]/DeliveryFoodApplication_RecommendationSystem](https://github.com/[PERSON]/DeliveryFoodApplication_RecommendationSystem)]([https://github.com/[PERSON]/DeliveryFoodApplication_RecommendationSystem](https://github.com/[PERSON]/DeliveryFoodApplication_RecommendationSystem)), accessed on 15 January 2023.  
Authors thank Torus Tecnologias SAPI de CV for providing the real dataset. In addition, the authors thank [PERSON] for helping to organize the related work.  
The authors declare no conflict of interest.