# Recommendation System for a Delivery Food Application Based on Number of Orders
## 1 Introduction  
Recently, we have witnessed unprecedented use of technology and its services [1]. The salient feature of the internet is its ubiquity, namely, the network is available at home, at the university, and on small portable devices (phones and watches), sensors, etc. [2]. However, thanks to the growth of internet services, we have a problem of information overload. Recommender systems are algorithms that contribute to the resolution of the problem of information explosion [3]. They have been created to assist people in deciding, from within a large group of options or an overload of information, the one that best suits their personality interests and preferences within a wide range of circumstances [4]. These have been successfully developed in many areas, such as movies [5, 6], books [7], education [8], music [9], patient diet [10], and online shopping [11, 12], to mention a few. Therefore, the particular interest of this research is related to food-delivery services since this type of business has increased considerably due to the COVID-19 pandemic [13]. For that reason, it is essential to generate methodologies for restaurant recommendations. The recommendation system helps customers to identify restaurants from an overwhelming group of options by matching customer preferences as much as possible [14]. For restaurants, the recommendation system helps restaurants make free advertising and increasetheir turnover [15]. Most food-delivery services are applications designed to be used on mobile devices, where the use of recommendation systems is critical since, with small screens, it is essential that what is shown is genuinely relevant for customers to help them in the decision process [16]. Recommendation systems typically target individuals and are based on individuals' viewing, purchasing, or rating history [17]. Specifically, the documents that propose restaurant recommendation systems are based on ratings or reviews proportioned by customers, using data from own-made apps [15; 17; 18] or platforms such as TripAdvisor, Zomato, Foursquare, or Yield [14; 16; 19; 20; 21; 22; 23]. However, only some customers gave ratings or reviews after their purchases, making implementing these systems in real food-delivery applications difficult. This document proposes a recommendation system whose input data are real sales transactions, which are always available and stored in all food-delivery applications. Most studies explored restaurant attributes on the overall customer experience (food, service, atmosphere, and value). However, when people buy through applications, they can only enjoy part of the experience in restaurants [24]. The data that all the food-delivery applications have from all customers consist of the history of orders. The number of orders reveals customer preferences. A customer who likes a particular restaurant tends to place several orders from the same place. Most recommendation systems are focused on predicting the rating proportioned by users [21; 25; 26] and use performance metrics such as accuracy, recall, mean squared error, etc. In this case, we want to predict which restaurants can be preferred by users. For that reason, we propose our own performance metric. To summarize, this research contributes to presenting a recommendation system for a food-delivery application that uses the number of orders as input. The system uses the nearest clients considering similar buying patterns to make a recommendation. Quite similar to [5; 27], our proposal calculates the similarity among customers. To the best of our knowledge, there are no recommendation systems based on the number of sales or orders. In addition to the recommendation system, we propose a methodology for validating this restaurant recommendation. We analyzed the performance of our proposal by using a dataset with real sales from 2019 to 2021, provided by a local food-delivery application (data available online).