Recommender systems are information filtering software which is capable of resolving the recent issue of internet’s information overload. The recommender system generate the recommendation more suitably based on the data gathered either implicitly like user profile, click information, web log history or explicitly like ratings (scale 1–5), likes, dislikes, feedbacks. The most important challenge to the recommender system is the growing number of online users making it a high dimensional data which leads to the data sparsity problem where the accuracy of recommendation depends on the availability of the data. In this paper, a new approach called formal concept analysis is employed to handle the high dimensional data and a FCA-based recommender algorithm, User-based concept clustering recommendation algorithm (UBCCRA) is proposed. The UBCCRA out performs by accurately generating the recommendation for the group-based users called cluster users. The experimental result is shown to prove the cluster recommendation with good result. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019.