Recommended systems are one of the most important techniques used to introduce information about user needs, including related services, by analyzing user actions [1, 2]. For the recommender system, a collaborative filtering approach is used to introduce information that will meet the needs of the user. The collaborative filtering is based on similarly tasteless users, the same choice, and the idea that users who buy in the past will buy in the future [3]. Data production factors for the collaborative filtering process are user interest or user behavior in the form of the feature vector. This vector is paired with all other user carriers, and the most similar users are selected to be made in the vicinity of the user. From there, the guide contains information about things previously liked by users in their neighborhood [4]. However, collaborative filtering often suffers from vulnerabilities [5] that affect the quality of their neighborhood. Use like Cold-start, Sparsity, and Rating credibility.

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This article investigates the efficiency of a doParallel algorithm and a formal concept analysis (FCA) network graph applied to recommendation systems. It is the first article using the FCA method to create a network graph and apply this graph to improve the accuracy of a recommendation system. According to the fundamental knowledge about users who have similar feature information, they may like the same items. This idea was used to create an FCA network graph. In the proposed process, the k-clique method was used to divide this network graph into various communities. A combination of the k-nearest neighbor and the betweenness centrality methods was used to find a suitable community for a new user based on the feature information similarities between the new user and an existing user in each community. Finally, a data mining method created a list of items from suitable communities and recommended them to the new user. In essence, the execution in this article uses a doParallel algorithm as a mechanism in parallel processing technology. The result of the implementation is satisfactory. It proved that the proposed method could resolve the cold-start problem in a recommendation system and may overcome the vast time consumption when a huge dataset is involved.

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