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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.

The Efficiency of a DoParallel Algorithm and an FCA Network Graph Applied to Recommendation System