# The Efficiency of a DoParallel Algorithm and an FCA Network Graph Applied to Recommendation System
## 1 Introduction
According to this reason, many researchers are tried to develop various new methods such as how to classify users into several communities by using their personal information or create a technique that can pick up the cluster of the users by using their connection [17; 18; 19; 20; 21]. Some work that concerning this research were presented by [PERSON] et al. [26], his work shown the opportunities and insights when social computing meets soft computing, and [PERSON] et al. [27] predicted the unemployment rate using social media analysis. One of those methodologies is the \(k\)-clique, it is typically used to analyze data of the users in the vast social network and classify them into various groups that depend on their personal information or their connections [25; 26; 27; 28; 29]. In the past few years, some researchers used the \(k\)-clique method as an algorithm model in the recommendation system [2]. The result of this idea presented a high accuracy. Simultaneously, the data mining is a technique of analyzing hidden patterns and relationships in data from vast amounts of data, and it automated using methods from statistics, machine learning, pattern recognition and mathematics used in the recommendation system. The data mining method is a trendy model in the field of the recommendation system. The technique of the data mining method, such as a classification method, a clustering method and the prediction method. Those methods gave a high-performance result. Moreover, another essential technology of the data mining method is an association rule mining method, which is quite favorable for analyzing the frequency of the item in the data transaction. There are various techniques for measuring the similarity method among the user in the network graph. These methods are widely discussed in many research articles. The commonly utilized methods are the \(k\)-nearest neighbor and betweenness centrality method. The \(k\)-nearest neighbor is a general algorithm that stores all existing cases and classifies new cases based on a similarity measurement [30; 31; 32; 33]. The betweenness centrality method is used to determine centrality in the network graph based on close paths [34; 35; 36]. The result of using both of these methods is quite satisfied. The doParallel mechanism is a parallel backend for the for-each package in the Parallel processing methodology. It provides a tool required to execute for-each loops in parallel processing. The for-each loops in parallel processing need to be used in conjunction with a container such as doParallel package instead of running code in parallel processing. The user needs to register a parallel backend to use. Differently, the for-each loop will perform tasks continuously, even when the %dopar% operator used. The doParallel uses multicore functionality on systems and snow component on Windows. Note that the multicore component only runs tasks on a single computer, not a cluster of machines. However, the snow function can use to run on a group using Unix operating systems such as Windows or even a combination. It is useless to use doParallel and parallel on a machine with a single processor with a single core. To improve the speed, it must work on devices with multiple processors, multiple cores or both. The for-each mechanism will issue a warning that it is working in sequence if there is no parallel backend registration. However, this warning will only be issued once. Therefore, to increase the accuracy further compared to the recent results in the recommendation system, to solve the problem of cold-start and the construction of new technique, instead of using the standard technology in the recommendation system, the problem of execution consuming a lot of time to access to the large-scale dataset, the efficiency of a doParallel algorithm and FCA network graph applied to the recommendation system will be examined in this article.