# The Efficiency of a DoParallel Algorithm and an FCA Network Graph Applied to Recommendation System
## 1 Introduction
### Problem Statement and Contribution  
The recommendation techniques are widely used in e-business, for example, Netflix's movie recommendation, Amazon's book recommendation, Pendura's music recommendations, Yahoo's news recommendation, etc. One of the major challenges in the effectiveness of the recommendation system is how to recommend products to the new users, and the long time of the execution in the large-scale dataset. The cold-start problem occurs when new users enter the system, or new items are added to the catalog. For instance, if the data information of the existing users and the rating of the new user items are unavailable, then the new users must rate enough things so that the system can save their settings correctly and gives reliable recommendations to the new users accordingly. Similarly, when new items are added to the system, they must be ranked by many users before being able to recommend users with similar interests to those that get ranked. The cold-start problem can be solved in many ways, including asking the user at the beginning to rate some items, asking the users explicitly to state their taste in aggregate, and suggesting items to the user based on the collected demographic information. In the case of the scalability problem, the rate of growth of both the number of items and the number of users, it becomes difficult for a typical recommender to process such large-scale data. For example, Amazon.com recommends more than 18 million items to more than 20 million customers. The scalability can be solved with different techniques, including clustering, reducing dimensionality and Bayesian network. The main contribution of this article is summarized as follows. This article proposed the efficiency of a doParallel algorithm and the FCA network graph applied to the recommendation system. The main objective is to solve the cold-start problem, which is the primary issue in the recommendation system, and to solve the execution consuming a lot of time during access to the item from the massive scale of the dataset. The process of this article is to start using the personalized information of the users in the movie lenses dataset to create the FCA network graph. Then, the FCA network graph was divided into various communities by using the technique of the \(k\)-clique method. The personalized information of a new user applied to calculate the similarity with the user in the diverse community. This process is a combination of the \(k\)-nearest neighbor method and the betweenness centrality method to assign suitable communities to a new user. First, the \(k\)-nearest neighbor method is used for finding suitable communities for a new user. Second, the betweenness centrality method is used as the same purpose as the \(k\)-nearest neighbor method. Finally, the suitable communities, which generated by the \(k\)-nearest neighbor method and betweenness centrality method is gathered together and assigned to a new user The association rule mining method as a mechanism of the data mining methodology is used for listing the name of the movies from appropriate communities. This list of the movies was recommended to a new user--the execution in this experiment used doParallel algorithm, which is a mechanism in parallel processing technology. This algorithm is used to split the proposed method algorithm into the various parts for the execution of them at the same time using the several cores of CPU. This idea can solve the problem of the execution consuming a lot of time to access the huge dataset. To assess the performance. The proposed method is compared to the two most successful methods used in the experiment: the collaborative filtering method and the maximal clique method. The workflow of the maximal clique method is; it divided users into various communities by using users' personalized information. Then, the new user will be assigned to a suitable community with the help of the combination method from the betweenness centrality method and the \(k\)-nearest neighbor method.