# The Efficiency of a DoParallel Algorithm and an FCA Network Graph Applied to Recommendation System
## 1 Introduction  
A recommendation system is a primary function in the field of business over the Internet or e-commerce [1; 2; 3]. It acts as a tool for filtering, prioritized and efficiently deliver relevant information that matches the requirement, interest and preference of their customer or users. In addition, this method can enhance the revenues of selling more product. It earns a lot of benefits to the business owner or service providers and users, especially in the field of online shopping environment, online movie, drop shipping, e-publishing, affiliate marketing, dating matching, search engine [4; 5], etc. In the scientific library, it supports users to move beyond catalog searches. Inside the methodology, there are various methods, such as content-based filtering, collaborative filtering and hybrid filtering [6]. Collaborative filtering is the most powerful and widely used technique in the recommendation system [7; 8; 9]. Collaborative filtering can be divided into two categories: memory-based and model-based [10; 11]. The sudden growth in the number of available digital information and the number of visitors to an e-commerce system has created a potential challenge of information overload, which handles timely access to items of interest on the Internet or execution consuming a lot of time from the large-scale of data. This issue has increased the demand for recommendation systems more than ever being in the past few years [12]. The cold-start problem is a well-known issue in the recommendation system. Thisissue is related to recommendations for new items or novel users. In the case of new users, the system does not have information about their preferences. Then it is hard to make recommendations for them [13]. Currently, many researchers created various methods to solve the cold-start problem, which is the primary issue in the recommender system, and almost all of these methods gave a satisfying result. But to increase more accuracy in predicting the item to match with the requirement of the user, this issue still needs more research and development [14]. Besides developing the new techniques to enhance the accuracy in the recommendation system, many researchers were developed new methods to provide a result with high accuracy in various research areas, such as formal concept analysis (FCA), social network analysis, data mining, similarity measuring method, parallel processing method, etc. Hence, therefore, Some of their important findings and proposed methods are briefly described below. FCA is a method of data analysis that describes the correlation among a particular set of attributes and a specific set of objects. It gained the attention of the researchers because the results using this method were completely satisfied. [PERSON] et al. [15] proposed new theory using conceptual graphs of FCA method to discovered knowledge in online drug transactions, [PERSON] et al. [16] used FCA method to analyze an email collection, [PERSON] et al. [17] combined the FCA method and the attribute partial order theory to create a visualization method of huge video/image dataset, [PERSON] et al. [18] proposed the relationship between FCA theory and rough set theory, [PERSON] et al. [19] proposed the reduction of FCA method based on modified Hamming distance, [PERSON] et al. [20] used FCA method to create the test suite reduction for mutation testing, [PERSON] et al. [21] used FCA method for performance evaluation of employees of an organization, [PERSON] et al. [22] tried to mining hypermethylated genes in breast cancer tumor subtypes using FCA method, [PERSON] et al. [23] used FCA method to detect the community in the social network with node attributes, [PERSON] et al. [24] proposed symptoms investigation using FCA method for enhancing medical diagnoses, [PERSON] et al. [25] proposed similarity evaluation between graphs using FCA method, etc. However, only a few research articles related to the FCA method are introduced to creating the network graph. Nowadays, the users in the social network are increasing rapidly.