In this digital world, the Internet is the major source of rich amount of data where there are large numbers of choices available to the internet users. It requires the attention to scrutinize, itemize and effectively project the essential information in order to violate the dispute of information overload. The information overload is an obstacle for many of the internet users. Recommender systems aim to provide solution to such obstacles by refining and seeking the large volume of exponentially growing information to provide the internet users with the personalized content and services. Here finding or predicting the user-item rating with the sparse data available in the rating matrix of the input data becomes a challenging task. This paper explores the problem of recommendation by presenting a FCA-based methodology which uses the mathematical tool in reducing the dimensionality of the user-item rating thereby determining the rating value of the unknown user with its corresponding item. The proposed method is simple and faster explained with an illustration to complete the user-item rating matrix from which the rating can be predicted by applying any of the recommendation algorithms.